Caso 6. Comparación modelos de regresión. Datos Precios autos. Programamción Python
Pablo Eduardo Martinez Solis
2022-11-01
1 Objetivo
Comparar modelos de supervisados a través de la aplicación de algoritmos de predicción de precios de automóviles determinando el estadístico del error cuadrático medio (rmse).
2 Descripción
Se cargan los datos previamente preparados de la dirección https://raw.githubusercontent.com/rpizarrog/Analisis-Inteligente-de-datos/main/datos/CarPrice_Assignment_Numericas_Preparado.csv
Se crean datos de entrenamiento al 80%
Se crean datos de validación al 20%
Se crea el modelo regresión múltiple con datos de entrenamiento
Con este modelo se responde a preguntas tales como:
¿cuáles son variables que están por encima del 90% de confianza como predictores?,
¿Cuál es el valor de R Square Adjusted o que tanto representan las variables dependientes al precio del vehículo?
Se generan predicciones con datos de validación
Se determina el estadístico RMSE para efectos de comparación
Se crea el modelo árboles de regresión con los datos de entrenamiento
Se identifica la importancia de las variables sobre el precio
Se visualiza el árbol de regresión y sus reglas de asociación
Se hacen predicciones con datos de validación
Se determinar el estadístico RMSE para efectos de comparación
Se construye el modelo bosques aleatorios con datos de entrenamiento y con 20 árboles simulados
Se identifica la importancia de las variables sobre el precio
Se generan predicciones con datos de validación
Se determina el estadístico RMSE para efectos de comparación
Al final del caso, se describe una interpretación personal
3 Desarrollo
3.1 Cargar librerías
# Tratamiento de datos
import numpy as np
import pandas as pd
# Gráficos
import matplotlib.pyplot as plt
# Preprocesado y moYdelado
from sklearn.model_selection import train_test_split
# Estadisticos y lineal múltiple
import statsmodels.api as sm # Estadísticas R Adjused
import seaborn as sns # Gráficos
from sklearn import linear_model
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures # Polinomial
# Arbol de regresion
from sklearn.tree import DecisionTreeRegressor
from sklearn.tree import plot_tree
from sklearn.tree import export_graphviz
from sklearn.tree import export_text
from sklearn.model_selection import GridSearchCV
# Random Forest
from sklearn.ensemble import RandomForestRegressor
# Metricas
from sklearn import metrics
from sklearn.metrics import mean_squared_error, r2_score3.2 Cargar datos
datos = pd.read_csv("https://raw.githubusercontent.com/rpizarrog/Analisis-Inteligente-de-datos/main/datos/CarPrice_Assignment_Numericas_Preparado.csv")
datos## Unnamed: 0 symboling wheelbase ... citympg highwaympg price
## 0 1 3 88.6 ... 21 27 13495.0
## 1 2 3 88.6 ... 21 27 16500.0
## 2 3 1 94.5 ... 19 26 16500.0
## 3 4 2 99.8 ... 24 30 13950.0
## 4 5 2 99.4 ... 18 22 17450.0
## .. ... ... ... ... ... ... ...
## 200 201 -1 109.1 ... 23 28 16845.0
## 201 202 -1 109.1 ... 19 25 19045.0
## 202 203 -1 109.1 ... 18 23 21485.0
## 203 204 -1 109.1 ... 26 27 22470.0
## 204 205 -1 109.1 ... 19 25 22625.0
##
## [205 rows x 16 columns]3.3 Exploración de datos
print("Observaciones y variables: ", datos.shape)## Observaciones y variables: (205, 16)print("Columnas y tipo de dato")
# datos.columns## Columnas y tipo de datodatos.dtypes## Unnamed: 0 int64
## symboling int64
## wheelbase float64
## carlength float64
## carwidth float64
## carheight float64
## curbweight int64
## enginesize int64
## boreratio float64
## stroke float64
## compressionratio float64
## horsepower int64
## peakrpm int64
## citympg int64
## highwaympg int64
## price float64
## dtype: object3.4 Diccionario de datos
| Col | Nombre | Descripción |
|---|---|---|
| 1 | Symboling | Its assigned insurance risk rating, A value of +3 indicates that the auto is risky, -3 that it is probably pretty safe.(Categorical) |
| 2 | wheelbase | Weelbase of car (Numeric). Distancia de ejes en pulgadas. |
| 3 | carlength | Length of car (Numeric). Longitud |
| 4 | carwidth | Width of car (Numeric). Amplitud |
| 5 | carheight | height of car (Numeric). Altura |
| 6 | curbweight | The weight of a car without occupants or baggage. (Numeric). Peso del auto |
| 7 | enginesize | Size of car (Numeric). Tamaño del carro en … |
| 8 | boreratio | Boreratio of car (Numeric). Eficiencia de motor |
| 9 | stroke | Stroke or volume inside the engine (Numeric). Pistones, tiempos, combustión |
| 10 | compressionratio | compression ratio of car (Numeric). Comprensión o medición de presión en motor |
| 11 | horsepower | Horsepower (Numeric). Poder del carro |
| 12 | peakrpm | car peak rpm (Numeric). Picos de revoluciones por minuto |
| 13 | citympg | Mileage in city (Numeric). Consumo de gasolina |
| 14 | highwaympg | Mileage on highway (Numeric). Consumo de gasolina |
| 16 | price (Dependent variable) |
Price of car (Numeric). Precio del carro en dólares |
~Fuente: https://archive.ics.uci.edu/ml/datasets/Automobile~
3.5 Limpiar datos
Dejar solo las variables necesarias:
‘symboling’, ‘wheelbase’, ‘carlength’, ‘carwidth’, ‘carheight’, ‘curbweight’, ‘enginesize’, ‘boreratio’, ‘stroke’, ‘compressionratio’, ‘horsepower’, ‘peakrpm’, ‘citympg’, ‘highwaympg’, ‘price’
datos = datos[['symboling', 'wheelbase', 'carlength', 'carwidth', 'carheight', 'curbweight', 'enginesize', 'boreratio', 'stroke', 'compressionratio', 'horsepower', 'peakrpm', 'citympg', 'highwaympg', 'price']]
datos.describe()## symboling wheelbase carlength ... citympg highwaympg price
## count 205.000000 205.000000 205.000000 ... 205.000000 205.000000 205.000000
## mean 0.834146 98.756585 174.049268 ... 25.219512 30.751220 13276.710571
## std 1.245307 6.021776 12.337289 ... 6.542142 6.886443 7988.852332
## min -2.000000 86.600000 141.100000 ... 13.000000 16.000000 5118.000000
## 25% 0.000000 94.500000 166.300000 ... 19.000000 25.000000 7788.000000
## 50% 1.000000 97.000000 173.200000 ... 24.000000 30.000000 10295.000000
## 75% 2.000000 102.400000 183.100000 ... 30.000000 34.000000 16503.000000
## max 3.000000 120.900000 208.100000 ... 49.000000 54.000000 45400.000000
##
## [8 rows x 15 columns]datos## symboling wheelbase carlength ... citympg highwaympg price
## 0 3 88.6 168.8 ... 21 27 13495.0
## 1 3 88.6 168.8 ... 21 27 16500.0
## 2 1 94.5 171.2 ... 19 26 16500.0
## 3 2 99.8 176.6 ... 24 30 13950.0
## 4 2 99.4 176.6 ... 18 22 17450.0
## .. ... ... ... ... ... ... ...
## 200 -1 109.1 188.8 ... 23 28 16845.0
## 201 -1 109.1 188.8 ... 19 25 19045.0
## 202 -1 109.1 188.8 ... 18 23 21485.0
## 203 -1 109.1 188.8 ... 26 27 22470.0
## 204 -1 109.1 188.8 ... 19 25 22625.0
##
## [205 rows x 15 columns]3.5.1 Datos de entrenamiento y validación
Datos de entrenamiento al 80% de los datos y 20% los datos de validación. Semilla 1349
X_entrena, X_valida, Y_entrena, Y_valida = train_test_split(datos.drop(columns = "price"), datos['price'],train_size = 0.80, random_state = 1349)3.5.1.1 Datos de entrenamiento
X_entrena## symboling wheelbase carlength ... peakrpm citympg highwaympg
## 179 3 102.9 183.5 ... 5200 19 24
## 28 -1 103.3 174.6 ... 5000 24 30
## 132 3 99.1 186.6 ... 5250 21 28
## 116 0 107.9 186.7 ... 4150 28 33
## 123 -1 103.3 174.6 ... 5000 24 30
## .. ... ... ... ... ... ... ...
## 194 -2 104.3 188.8 ... 5400 23 28
## 164 1 94.5 168.7 ... 4800 29 34
## 17 0 110.0 197.0 ... 5400 15 20
## 126 3 89.5 168.9 ... 5900 17 25
## 18 2 88.4 141.1 ... 5100 47 53
##
## [164 rows x 14 columns]3.5.1.2 Datos de validación
X_valida## symboling wheelbase carlength ... peakrpm citympg highwaympg
## 154 0 95.7 169.7 ... 4800 27 32
## 147 0 97.0 173.5 ... 5200 25 31
## 104 3 91.3 170.7 ... 5200 19 25
## 102 0 100.4 184.6 ... 5200 17 22
## 61 1 98.8 177.8 ... 4800 26 32
## 163 1 94.5 168.7 ... 4800 29 34
## 124 3 95.9 173.2 ... 5000 19 24
## 7 1 105.8 192.7 ... 5500 19 25
## 169 2 98.4 176.2 ... 4800 24 30
## 16 0 103.5 193.8 ... 5400 16 22
## 15 0 103.5 189.0 ... 5400 16 22
## 73 0 120.9 208.1 ... 4500 14 16
## 187 2 97.3 171.7 ... 4500 37 42
## 109 0 114.2 198.9 ... 5000 19 24
## 52 1 93.1 159.1 ... 5000 31 38
## 111 0 107.9 186.7 ... 5000 19 24
## 80 3 96.3 173.0 ... 5500 23 30
## 103 0 100.4 184.6 ... 5200 19 25
## 75 1 102.7 178.4 ... 5000 19 24
## 10 2 101.2 176.8 ... 5800 23 29
## 54 1 93.1 166.8 ... 5000 31 38
## 40 0 96.5 175.4 ... 5800 27 33
## 57 3 95.3 169.0 ... 6000 17 23
## 66 0 104.9 175.0 ... 4200 31 39
## 137 2 99.1 186.6 ... 5500 19 26
## 161 0 95.7 166.3 ... 4800 28 34
## 158 0 95.7 166.3 ... 4500 34 36
## 11 0 101.2 176.8 ... 5800 23 29
## 171 2 98.4 176.2 ... 4800 24 30
## 2 1 94.5 171.2 ... 5000 19 26
## 177 -1 102.4 175.6 ... 4200 27 32
## 184 2 97.3 171.7 ... 4800 37 46
## 21 1 93.7 157.3 ... 5500 37 41
## 82 3 95.9 173.2 ... 5000 19 24
## 125 3 94.5 168.9 ... 5500 19 27
## 6 1 105.8 192.7 ... 5500 19 25
## 65 0 104.9 175.0 ... 5000 19 27
## 48 0 113.0 199.6 ... 4750 15 19
## 42 1 96.5 169.1 ... 5500 25 31
## 27 1 93.7 157.3 ... 5500 24 30
## 79 1 93.0 157.3 ... 5500 24 30
##
## [41 rows x 14 columns]3.6 Modelos Supervisados
3.6.1 Modelo de regresión lineal múltiple. (RM)
Se construye el modelo de regresión lineal múltiple (rm)
modelo_rm = LinearRegression()
modelo_rm.fit(X_entrena,Y_entrena)LinearRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LinearRegression()
3.6.1.1 Coeficientes
Solo se muestran los coeficientes de: \(\beta_1, \beta_2, ...\beta_n\)
modelo_rm.coef_## array([ 3.43157155e+02, 1.51903870e+01, -1.13534712e+02, 7.70362919e+02,
## 3.23536794e+02, 2.96799050e+00, 1.06906358e+02, -1.72051534e+03,
## -2.99694085e+03, 2.51940047e+02, 3.52827086e+01, 1.70779936e+00,
## -1.80810204e+02, 9.42771950e+01])- En modelos lineales múltiples el estadístico Adjusted R-squared: 0.8347 significa que las variables independientes explican aproximadamente el 83.47% de la variable dependiente precio.
print(modelo_rm.score(X_entrena, Y_entrena))## 0.86866326999111413.6.1.2 Predicciones del modelo rm
predicciones_rm = modelo_rm.predict(X_valida)
print(predicciones_rm[:-1])## [ 7425.97160548 10895.20899664 23933.35662532 22920.43759406
## 10065.07126253 6220.22581519 15712.58745231 19094.54494932
## 13151.99635195 26110.09792267 26086.57663728 38915.62637259
## 10214.92858711 14676.02944758 6598.50866682 17784.71681822
## 10262.34855333 21817.66621855 19100.88650115 12172.2469208
## 5771.82518678 7322.07510171 8332.73519914 13134.35459405
## 16833.14707877 6581.9476114 8785.55759535 11485.93261131
## 13635.77880361 18659.23590762 8754.65533182 10376.61435969
## 5261.83182286 15774.31246318 20408.53583706 18768.06599421
## 15128.55952034 30135.37992201 10019.21398334 8461.63499463]3.6.1.3 Tabla comparativa
comparaciones = pd.DataFrame(X_valida)
comparaciones = comparaciones.assign(Precio_Real = Y_valida)
comparaciones = comparaciones.assign(Precio_Prediccion = predicciones_rm.flatten().tolist())
print(comparaciones)## symboling wheelbase ... Precio_Real Precio_Prediccion
## 154 0 95.7 ... 7898.0 7425.971605
## 147 0 97.0 ... 10198.0 10895.208997
## 104 3 91.3 ... 17199.0 23933.356625
## 102 0 100.4 ... 14399.0 22920.437594
## 61 1 98.8 ... 10595.0 10065.071263
## 163 1 94.5 ... 8058.0 6220.225815
## 124 3 95.9 ... 12764.0 15712.587452
## 7 1 105.8 ... 18920.0 19094.544949
## 169 2 98.4 ... 9989.0 13151.996352
## 16 0 103.5 ... 41315.0 26110.097923
## 15 0 103.5 ... 30760.0 26086.576637
## 73 0 120.9 ... 40960.0 38915.626373
## 187 2 97.3 ... 9495.0 10214.928587
## 109 0 114.2 ... 12440.0 14676.029448
## 52 1 93.1 ... 6795.0 6598.508667
## 111 0 107.9 ... 15580.0 17784.716818
## 80 3 96.3 ... 9959.0 10262.348553
## 103 0 100.4 ... 13499.0 21817.666219
## 75 1 102.7 ... 16503.0 19100.886501
## 10 2 101.2 ... 16430.0 12172.246921
## 54 1 93.1 ... 7395.0 5771.825187
## 40 0 96.5 ... 10295.0 7322.075102
## 57 3 95.3 ... 13645.0 8332.735199
## 66 0 104.9 ... 18344.0 13134.354594
## 137 2 99.1 ... 18620.0 16833.147079
## 161 0 95.7 ... 8358.0 6581.947611
## 158 0 95.7 ... 7898.0 8785.557595
## 11 0 101.2 ... 16925.0 11485.932611
## 171 2 98.4 ... 11549.0 13635.778804
## 2 1 94.5 ... 16500.0 18659.235908
## 177 -1 102.4 ... 11248.0 8754.655332
## 184 2 97.3 ... 7995.0 10376.614360
## 21 1 93.7 ... 5572.0 5261.831823
## 82 3 95.9 ... 12629.0 15774.312463
## 125 3 94.5 ... 22018.0 20408.535837
## 6 1 105.8 ... 17710.0 18768.065994
## 65 0 104.9 ... 18280.0 15128.559520
## 48 0 113.0 ... 35550.0 30135.379922
## 42 1 96.5 ... 10345.0 10019.213983
## 27 1 93.7 ... 8558.0 8461.634995
## 79 1 93.0 ... 7689.0 8379.181520
##
## [41 rows x 16 columns]3.6.1.4 RMSE modelo de rm
rmse_rm = mean_squared_error(
y_true = Y_valida,
y_pred = predicciones_rm,
squared = False
)
print(f"El error (rmse) de test es: {rmse_rm}")## El error (rmse) de test es: 4075.0809995218374o
print('Root Mean Squared Error RMSE:', np.sqrt(metrics.mean_squared_error(Y_valida, predicciones_rm)))## Root Mean Squared Error RMSE: 4075.08099952183743.6.2 Modelo de árbol de regresión (AR)
Se construye el modelo de árbol de regresión (ar)
modelo_ar = DecisionTreeRegressor(
#max_depth = 3,
random_state = 1349
)Entrenar el modelo
modelo_ar.fit(X_entrena, Y_entrena)DecisionTreeRegressor(random_state=1349)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeRegressor(random_state=1349)
3.6.2.1 Visualización de árbol de regresión
fig, ax = plt.subplots(figsize=(12, 5))
print(f"Profundidad del árbol: {modelo_ar.get_depth()}")## Profundidad del árbol: 13print(f"Número de nodos terminales: {modelo_ar.get_n_leaves()}")## Número de nodos terminales: 152plot = plot_tree(
decision_tree = modelo_ar,
feature_names = datos.drop(columns = "price").columns,
class_names = 'price',
filled = True,
impurity = False,
fontsize = 10,
precision = 2,
ax = ax
)
plot
Reglas de asociación del árbol
texto_modelo = export_text(
decision_tree = modelo_ar,
feature_names = list(datos.drop(columns = "price").columns)
)
print(texto_modelo)## |--- enginesize <= 182.00
## | |--- curbweight <= 2544.00
## | | |--- curbweight <= 2216.50
## | | | |--- horsepower <= 68.50
## | | | | |--- curbweight <= 1987.00
## | | | | | |--- stroke <= 3.11
## | | | | | | |--- highwaympg <= 47.50
## | | | | | | | |--- horsepower <= 61.00
## | | | | | | | | |--- value: [5399.00]
## | | | | | | | |--- horsepower > 61.00
## | | | | | | | | |--- value: [5348.00]
## | | | | | | |--- highwaympg > 47.50
## | | | | | | | |--- value: [5151.00]
## | | | | | |--- stroke > 3.11
## | | | | | | |--- highwaympg <= 34.50
## | | | | | | | |--- value: [5195.00]
## | | | | | | |--- highwaympg > 34.50
## | | | | | | | |--- citympg <= 34.00
## | | | | | | | | |--- carlength <= 162.95
## | | | | | | | | | |--- curbweight <= 1888.00
## | | | | | | | | | | |--- value: [6377.00]
## | | | | | | | | | |--- curbweight > 1888.00
## | | | | | | | | | | |--- stroke <= 3.19
## | | | | | | | | | | | |--- value: [6095.00]
## | | | | | | | | | | |--- stroke > 3.19
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | | | | |--- carlength > 162.95
## | | | | | | | | | |--- value: [6695.00]
## | | | | | | | |--- citympg > 34.00
## | | | | | | | | |--- peakrpm <= 5150.00
## | | | | | | | | | |--- value: [6479.00]
## | | | | | | | | |--- peakrpm > 5150.00
## | | | | | | | | | |--- enginesize <= 91.00
## | | | | | | | | | | |--- value: [5572.00]
## | | | | | | | | | |--- enginesize > 91.00
## | | | | | | | | | | |--- value: [5389.00]
## | | | | |--- curbweight > 1987.00
## | | | | | |--- stroke <= 3.13
## | | | | | | |--- curbweight <= 2027.50
## | | | | | | | |--- value: [6488.00]
## | | | | | | |--- curbweight > 2027.50
## | | | | | | | |--- value: [6338.00]
## | | | | | |--- stroke > 3.13
## | | | | | | |--- curbweight <= 2104.00
## | | | | | | | |--- carheight <= 50.70
## | | | | | | | | |--- value: [7150.50]
## | | | | | | | |--- carheight > 50.70
## | | | | | | | | |--- horsepower <= 61.50
## | | | | | | | | | |--- value: [7099.00]
## | | | | | | | | |--- horsepower > 61.50
## | | | | | | | | | |--- carlength <= 162.30
## | | | | | | | | | | |--- value: [6669.00]
## | | | | | | | | | |--- carlength > 162.30
## | | | | | | | | | | |--- value: [6692.00]
## | | | | | | |--- curbweight > 2104.00
## | | | | | | | |--- value: [7609.00]
## | | | |--- horsepower > 68.50
## | | | | |--- carwidth <= 63.50
## | | | | | |--- value: [5118.00]
## | | | | |--- carwidth > 63.50
## | | | | | |--- curbweight <= 2124.00
## | | | | | | |--- carheight <= 53.60
## | | | | | | | |--- carwidth <= 63.85
## | | | | | | | | |--- carlength <= 157.35
## | | | | | | | | | |--- symboling <= 0.50
## | | | | | | | | | | |--- value: [8916.50]
## | | | | | | | | | |--- symboling > 0.50
## | | | | | | | | | | |--- value: [7605.75]
## | | | | | | | | |--- carlength > 157.35
## | | | | | | | | | |--- symboling <= 0.50
## | | | | | | | | | | |--- value: [6575.00]
## | | | | | | | | | |--- symboling > 0.50
## | | | | | | | | | | |--- wheelbase <= 94.80
## | | | | | | | | | | | |--- truncated branch of depth 3
## | | | | | | | | | | |--- wheelbase > 94.80
## | | | | | | | | | | | |--- value: [8249.00]
## | | | | | | | |--- carwidth > 63.85
## | | | | | | | | |--- highwaympg <= 42.50
## | | | | | | | | | |--- curbweight <= 1948.00
## | | | | | | | | | | |--- carlength <= 147.30
## | | | | | | | | | | | |--- value: [6855.00]
## | | | | | | | | | | |--- carlength > 147.30
## | | | | | | | | | | | |--- value: [6529.00]
## | | | | | | | | | |--- curbweight > 1948.00
## | | | | | | | | | | |--- carheight <= 52.90
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | | | | | | |--- carheight > 52.90
## | | | | | | | | | | | |--- value: [6938.00]
## | | | | | | | | |--- highwaympg > 42.50
## | | | | | | | | | |--- value: [7738.00]
## | | | | | | |--- carheight > 53.60
## | | | | | | | |--- curbweight <= 1903.50
## | | | | | | | | |--- value: [5499.00]
## | | | | | | | |--- curbweight > 1903.50
## | | | | | | | | |--- curbweight <= 1944.50
## | | | | | | | | | |--- curbweight <= 1928.00
## | | | | | | | | | | |--- value: [6649.00]
## | | | | | | | | | |--- curbweight > 1928.00
## | | | | | | | | | | |--- value: [6849.00]
## | | | | | | | | |--- curbweight > 1944.50
## | | | | | | | | | |--- stroke <= 2.97
## | | | | | | | | | | |--- value: [7053.00]
## | | | | | | | | | |--- stroke > 2.97
## | | | | | | | | | | |--- highwaympg <= 35.50
## | | | | | | | | | | | |--- value: [7295.00]
## | | | | | | | | | | |--- highwaympg > 35.50
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | |--- curbweight > 2124.00
## | | | | | | |--- horsepower <= 76.00
## | | | | | | | |--- carheight <= 52.70
## | | | | | | | | |--- value: [8238.00]
## | | | | | | | |--- carheight > 52.70
## | | | | | | | | |--- value: [9258.00]
## | | | | | | |--- horsepower > 76.00
## | | | | | | | |--- citympg <= 30.00
## | | | | | | | | |--- curbweight <= 2210.50
## | | | | | | | | | |--- symboling <= 0.50
## | | | | | | | | | | |--- value: [7775.00]
## | | | | | | | | | |--- symboling > 0.50
## | | | | | | | | | | |--- carwidth <= 64.65
## | | | | | | | | | | | |--- value: [7957.00]
## | | | | | | | | | | |--- carwidth > 64.65
## | | | | | | | | | | | |--- value: [7975.00]
## | | | | | | | | |--- curbweight > 2210.50
## | | | | | | | | | |--- value: [8195.00]
## | | | | | | | |--- citympg > 30.00
## | | | | | | | | |--- value: [7126.00]
## | | |--- curbweight > 2216.50
## | | | |--- citympg <= 21.00
## | | | | |--- enginesize <= 75.00
## | | | | | |--- value: [11395.00]
## | | | | |--- enginesize > 75.00
## | | | | | |--- boreratio <= 3.26
## | | | | | | |--- value: [15250.00]
## | | | | | |--- boreratio > 3.26
## | | | | | | |--- value: [15645.00]
## | | | |--- citympg > 21.00
## | | | | |--- horsepower <= 89.00
## | | | | | |--- carwidth <= 66.00
## | | | | | | |--- horsepower <= 80.00
## | | | | | | | |--- curbweight <= 2277.50
## | | | | | | | | |--- curbweight <= 2250.50
## | | | | | | | | | |--- value: [7603.00]
## | | | | | | | | |--- curbweight > 2250.50
## | | | | | | | | | |--- peakrpm <= 4650.00
## | | | | | | | | | | |--- value: [7788.00]
## | | | | | | | | | |--- peakrpm > 4650.00
## | | | | | | | | | | |--- value: [7775.00]
## | | | | | | | |--- curbweight > 2277.50
## | | | | | | | | |--- horsepower <= 70.00
## | | | | | | | | | |--- value: [6918.00]
## | | | | | | | | |--- horsepower > 70.00
## | | | | | | | | | |--- value: [6785.00]
## | | | | | | |--- horsepower > 80.00
## | | | | | | | |--- carheight <= 53.15
## | | | | | | | | |--- carheight <= 50.50
## | | | | | | | | | |--- value: [8499.00]
## | | | | | | | | |--- carheight > 50.50
## | | | | | | | | | |--- curbweight <= 2385.00
## | | | | | | | | | | |--- carheight <= 52.30
## | | | | | | | | | | | |--- value: [6989.00]
## | | | | | | | | | | |--- carheight > 52.30
## | | | | | | | | | | | |--- value: [7463.00]
## | | | | | | | | | |--- curbweight > 2385.00
## | | | | | | | | | | |--- value: [8189.00]
## | | | | | | | |--- carheight > 53.15
## | | | | | | | | |--- curbweight <= 2255.50
## | | | | | | | | | |--- value: [7895.00]
## | | | | | | | | |--- curbweight > 2255.50
## | | | | | | | | | |--- citympg <= 23.50
## | | | | | | | | | | |--- value: [8013.00]
## | | | | | | | | | |--- citympg > 23.50
## | | | | | | | | | | |--- curbweight <= 2282.00
## | | | | | | | | | | | |--- value: [8495.00]
## | | | | | | | | | | |--- curbweight > 2282.00
## | | | | | | | | | | | |--- truncated branch of depth 3
## | | | | | |--- carwidth > 66.00
## | | | | | | |--- curbweight <= 2417.50
## | | | | | | | |--- carheight <= 54.60
## | | | | | | | | |--- value: [8845.00]
## | | | | | | | |--- carheight > 54.60
## | | | | | | | | |--- value: [9370.00]
## | | | | | | |--- curbweight > 2417.50
## | | | | | | | |--- citympg <= 28.00
## | | | | | | | | |--- value: [11245.00]
## | | | | | | | |--- citympg > 28.00
## | | | | | | | | |--- horsepower <= 68.50
## | | | | | | | | | |--- value: [10795.00]
## | | | | | | | | |--- horsepower > 68.50
## | | | | | | | | | |--- value: [10698.00]
## | | | | |--- horsepower > 89.00
## | | | | | |--- carheight <= 54.00
## | | | | | | |--- curbweight <= 2538.00
## | | | | | | | |--- horsepower <= 103.00
## | | | | | | | | |--- enginesize <= 127.00
## | | | | | | | | | |--- stroke <= 3.02
## | | | | | | | | | | |--- value: [9960.00]
## | | | | | | | | | |--- stroke > 3.02
## | | | | | | | | | | |--- highwaympg <= 30.50
## | | | | | | | | | | | |--- value: [9980.00]
## | | | | | | | | | | |--- highwaympg > 30.50
## | | | | | | | | | | | |--- value: [9988.00]
## | | | | | | | | |--- enginesize > 127.00
## | | | | | | | | | |--- value: [9895.00]
## | | | | | | | |--- horsepower > 103.00
## | | | | | | | | |--- curbweight <= 2469.50
## | | | | | | | | | |--- curbweight <= 2351.50
## | | | | | | | | | | |--- curbweight <= 2282.50
## | | | | | | | | | | | |--- value: [9298.00]
## | | | | | | | | | | |--- curbweight > 2282.50
## | | | | | | | | | | | |--- value: [9538.00]
## | | | | | | | | | |--- curbweight > 2351.50
## | | | | | | | | | | |--- value: [9279.00]
## | | | | | | | | |--- curbweight > 2469.50
## | | | | | | | | | |--- value: [9639.00]
## | | | | | | |--- curbweight > 2538.00
## | | | | | | | |--- value: [8449.00]
## | | | | | |--- carheight > 54.00
## | | | | | | |--- highwaympg <= 31.00
## | | | | | | | |--- carlength <= 173.70
## | | | | | | | | |--- horsepower <= 100.50
## | | | | | | | | | |--- value: [11595.00]
## | | | | | | | | |--- horsepower > 100.50
## | | | | | | | | | |--- value: [11259.00]
## | | | | | | | |--- carlength > 173.70
## | | | | | | | | |--- stroke <= 3.49
## | | | | | | | | | |--- value: [13950.00]
## | | | | | | | | |--- stroke > 3.49
## | | | | | | | | | |--- value: [12945.00]
## | | | | | | |--- highwaympg > 31.00
## | | | | | | | |--- highwaympg <= 33.00
## | | | | | | | | |--- symboling <= 0.50
## | | | | | | | | | |--- value: [10898.00]
## | | | | | | | | |--- symboling > 0.50
## | | | | | | | | | |--- value: [9995.00]
## | | | | | | | |--- highwaympg > 33.00
## | | | | | | | | |--- curbweight <= 2313.00
## | | | | | | | | | |--- value: [9549.00]
## | | | | | | | | |--- curbweight > 2313.00
## | | | | | | | | | |--- peakrpm <= 4700.00
## | | | | | | | | | | |--- value: [8948.00]
## | | | | | | | | | |--- peakrpm > 4700.00
## | | | | | | | | | | |--- value: [8949.00]
## | |--- curbweight > 2544.00
## | | |--- carwidth <= 68.60
## | | | |--- horsepower <= 118.50
## | | | | |--- horsepower <= 92.50
## | | | | | |--- carwidth <= 66.70
## | | | | | | |--- compressionratio <= 9.10
## | | | | | | | |--- carlength <= 175.60
## | | | | | | | | |--- value: [8778.00]
## | | | | | | | |--- carlength > 175.60
## | | | | | | | | |--- value: [9295.00]
## | | | | | | |--- compressionratio > 9.10
## | | | | | | | |--- value: [11048.00]
## | | | | | |--- carwidth > 66.70
## | | | | | | |--- horsepower <= 78.00
## | | | | | | | |--- value: [13845.00]
## | | | | | | |--- horsepower > 78.00
## | | | | | | | |--- value: [12290.00]
## | | | | |--- horsepower > 92.50
## | | | | | |--- curbweight <= 2701.00
## | | | | | | |--- boreratio <= 3.50
## | | | | | | | |--- peakrpm <= 5250.00
## | | | | | | | | |--- value: [14997.50]
## | | | | | | | |--- peakrpm > 5250.00
## | | | | | | | | |--- value: [13295.00]
## | | | | | | |--- boreratio > 3.50
## | | | | | | | |--- carheight <= 53.45
## | | | | | | | | |--- value: [11199.00]
## | | | | | | | |--- carheight > 53.45
## | | | | | | | | |--- curbweight <= 2676.50
## | | | | | | | | | |--- citympg <= 22.00
## | | | | | | | | | | |--- value: [11850.00]
## | | | | | | | | | |--- citympg > 22.00
## | | | | | | | | | | |--- value: [11694.00]
## | | | | | | | | |--- curbweight > 2676.50
## | | | | | | | | | |--- value: [12170.00]
## | | | | | |--- curbweight > 2701.00
## | | | | | | |--- carlength <= 181.60
## | | | | | | | |--- stroke <= 3.45
## | | | | | | | | |--- value: [17450.00]
## | | | | | | | |--- stroke > 3.45
## | | | | | | | | |--- value: [17669.00]
## | | | | | | |--- carlength > 181.60
## | | | | | | | |--- curbweight <= 3038.00
## | | | | | | | | |--- curbweight <= 2977.50
## | | | | | | | | | |--- curbweight <= 2923.50
## | | | | | | | | | | |--- symboling <= 0.00
## | | | | | | | | | | | |--- value: [12940.00]
## | | | | | | | | | | |--- symboling > 0.00
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | | | | | |--- curbweight > 2923.50
## | | | | | | | | | | |--- value: [15985.00]
## | | | | | | | | |--- curbweight > 2977.50
## | | | | | | | | | |--- enginesize <= 130.50
## | | | | | | | | | | |--- value: [11900.00]
## | | | | | | | | | |--- enginesize > 130.50
## | | | | | | | | | | |--- value: [13415.00]
## | | | | | | | |--- curbweight > 3038.00
## | | | | | | | | |--- curbweight <= 3224.50
## | | | | | | | | | |--- peakrpm <= 4575.00
## | | | | | | | | | | |--- value: [13200.00]
## | | | | | | | | | |--- peakrpm > 4575.00
## | | | | | | | | | | |--- highwaympg <= 26.00
## | | | | | | | | | | | |--- value: [16630.00]
## | | | | | | | | | | |--- highwaympg > 26.00
## | | | | | | | | | | | |--- value: [16515.00]
## | | | | | | | | |--- curbweight > 3224.50
## | | | | | | | | | |--- curbweight <= 3357.50
## | | | | | | | | | | |--- compressionratio <= 14.70
## | | | | | | | | | | | |--- value: [16695.00]
## | | | | | | | | | | |--- compressionratio > 14.70
## | | | | | | | | | | | |--- value: [17425.00]
## | | | | | | | | | |--- curbweight > 3357.50
## | | | | | | | | | | |--- curbweight <= 3457.50
## | | | | | | | | | | | |--- value: [13860.00]
## | | | | | | | | | | |--- curbweight > 3457.50
## | | | | | | | | | | | |--- value: [17075.00]
## | | | |--- horsepower > 118.50
## | | | | |--- horsepower <= 131.50
## | | | | | |--- wheelbase <= 102.35
## | | | | | | |--- curbweight <= 2737.50
## | | | | | | | |--- value: [20970.00]
## | | | | | | |--- curbweight > 2737.50
## | | | | | | | |--- value: [21105.00]
## | | | | | |--- wheelbase > 102.35
## | | | | | | |--- value: [24565.00]
## | | | | |--- horsepower > 131.50
## | | | | | |--- horsepower <= 158.00
## | | | | | | |--- curbweight <= 3112.50
## | | | | | | | |--- boreratio <= 3.59
## | | | | | | | | |--- carlength <= 177.45
## | | | | | | | | | |--- curbweight <= 2923.50
## | | | | | | | | | | |--- value: [14869.00]
## | | | | | | | | | |--- curbweight > 2923.50
## | | | | | | | | | | |--- value: [14489.00]
## | | | | | | | | |--- carlength > 177.45
## | | | | | | | | | |--- value: [13499.00]
## | | | | | | | |--- boreratio > 3.59
## | | | | | | | | |--- value: [12964.00]
## | | | | | | |--- curbweight > 3112.50
## | | | | | | | |--- carheight <= 55.05
## | | | | | | | | |--- enginesize <= 166.00
## | | | | | | | | | |--- value: [15750.00]
## | | | | | | | | |--- enginesize > 166.00
## | | | | | | | | | |--- value: [15690.00]
## | | | | | | | |--- carheight > 55.05
## | | | | | | | | |--- value: [18150.00]
## | | | | | |--- horsepower > 158.00
## | | | | | | |--- compressionratio <= 9.15
## | | | | | | | |--- carlength <= 174.45
## | | | | | | | | |--- value: [19699.00]
## | | | | | | | |--- carlength > 174.45
## | | | | | | | | |--- curbweight <= 3148.00
## | | | | | | | | | |--- peakrpm <= 5350.00
## | | | | | | | | | | |--- curbweight <= 3092.00
## | | | | | | | | | | | |--- value: [18420.00]
## | | | | | | | | | | |--- curbweight > 3092.00
## | | | | | | | | | | | |--- value: [18399.00]
## | | | | | | | | | |--- peakrpm > 5350.00
## | | | | | | | | | | |--- stroke <= 3.24
## | | | | | | | | | | | |--- value: [18150.00]
## | | | | | | | | | | |--- stroke > 3.24
## | | | | | | | | | | | |--- value: [17859.17]
## | | | | | | | | |--- curbweight > 3148.00
## | | | | | | | | | |--- value: [18950.00]
## | | | | | | |--- compressionratio > 9.15
## | | | | | | | |--- citympg <= 19.50
## | | | | | | | | |--- value: [15998.00]
## | | | | | | | |--- citympg > 19.50
## | | | | | | | | |--- value: [16558.00]
## | | |--- carwidth > 68.60
## | | | |--- curbweight <= 3055.50
## | | | | |--- enginesize <= 157.00
## | | | | | |--- peakrpm <= 5350.00
## | | | | | | |--- value: [19045.00]
## | | | | | |--- peakrpm > 5350.00
## | | | | | | |--- value: [16845.00]
## | | | | |--- enginesize > 157.00
## | | | | | |--- value: [21485.00]
## | | | |--- curbweight > 3055.50
## | | | | |--- compressionratio <= 8.90
## | | | | | |--- value: [23875.00]
## | | | | |--- compressionratio > 8.90
## | | | | | |--- horsepower <= 110.00
## | | | | | | |--- value: [22470.00]
## | | | | | |--- horsepower > 110.00
## | | | | | | |--- value: [22625.00]
## |--- enginesize > 182.00
## | |--- highwaympg <= 16.50
## | | |--- value: [45400.00]
## | |--- highwaympg > 16.50
## | | |--- compressionratio <= 16.50
## | | | |--- carwidth <= 72.00
## | | | | |--- curbweight <= 4008.00
## | | | | | |--- curbweight <= 2778.00
## | | | | | | |--- value: [33278.00]
## | | | | | |--- curbweight > 2778.00
## | | | | | | |--- boreratio <= 3.50
## | | | | | | | |--- carwidth <= 71.10
## | | | | | | | | |--- value: [35056.00]
## | | | | | | | |--- carwidth > 71.10
## | | | | | | | | |--- value: [34184.00]
## | | | | | | |--- boreratio > 3.50
## | | | | | | | |--- enginesize <= 267.50
## | | | | | | | | |--- boreratio <= 3.68
## | | | | | | | | | |--- value: [36880.00]
## | | | | | | | | |--- boreratio > 3.68
## | | | | | | | | | |--- value: [37028.00]
## | | | | | | | |--- enginesize > 267.50
## | | | | | | | | |--- value: [36000.00]
## | | | | |--- curbweight > 4008.00
## | | | | | |--- value: [32250.00]
## | | | |--- carwidth > 72.00
## | | | | |--- value: [31400.50]
## | | |--- compressionratio > 16.50
## | | | |--- carlength <= 196.75
## | | | | |--- curbweight <= 3632.50
## | | | | | |--- curbweight <= 3505.00
## | | | | | | |--- value: [28176.00]
## | | | | | |--- curbweight > 3505.00
## | | | | | | |--- value: [25552.00]
## | | | | |--- curbweight > 3632.50
## | | | | | |--- value: [28248.00]
## | | | |--- carlength > 196.75
## | | | | |--- value: [31600.00]3.6.2.2 Importancia de los predictores
importancia_predictores = pd.DataFrame(
{'predictor': datos.drop(columns = "price").columns,
'importancia': modelo_ar.feature_importances_}
)
print("Importancia de los predictores en el modelo")## Importancia de los predictores en el modeloimportancia_predictores.sort_values('importancia', ascending=False)## predictor importancia
## 6 enginesize 0.654449
## 5 curbweight 0.243935
## 10 horsepower 0.034996
## 3 carwidth 0.021673
## 13 highwaympg 0.017511
## 9 compressionratio 0.011175
## 12 citympg 0.006409
## 2 carlength 0.003051
## 7 boreratio 0.001889
## 4 carheight 0.001851
## 11 peakrpm 0.001283
## 1 wheelbase 0.000855
## 0 symboling 0.000666
## 8 stroke 0.000258Estos sería los predictores más importantes para el modelo de árbol de regresión enginesize, curbweight, peakrpm, carheight y wheelbase
3.6.2.3 Predicciones del modelo (ar)
predicciones_ar = modelo_ar.predict(X = X_valida)
predicciones_ar## array([ 6918., 9960., 19699., 18150., 8845., 8238., 14869., 16845.,
## 11199., 36880., 36880., 45400., 6918., 16695., 6095., 16630.,
## 9279., 13499., 18420., 13950., 6695., 8845., 11395., 11048.,
## 18150., 7198., 7788., 13950., 17669., 14869., 9988., 7775.,
## 5572., 14869., 12964., 16845., 24565., 32250., 9988., 7957.,
## 7957.])3.6.2.4 Tabla comparativa
comparaciones = pd.DataFrame(X_valida)
comparaciones = comparaciones.assign(Precio_Real = Y_valida)
comparaciones = comparaciones.assign(Precio_Prediccion = predicciones_ar.flatten().tolist())
print(comparaciones)## symboling wheelbase ... Precio_Real Precio_Prediccion
## 154 0 95.7 ... 7898.0 6918.0
## 147 0 97.0 ... 10198.0 9960.0
## 104 3 91.3 ... 17199.0 19699.0
## 102 0 100.4 ... 14399.0 18150.0
## 61 1 98.8 ... 10595.0 8845.0
## 163 1 94.5 ... 8058.0 8238.0
## 124 3 95.9 ... 12764.0 14869.0
## 7 1 105.8 ... 18920.0 16845.0
## 169 2 98.4 ... 9989.0 11199.0
## 16 0 103.5 ... 41315.0 36880.0
## 15 0 103.5 ... 30760.0 36880.0
## 73 0 120.9 ... 40960.0 45400.0
## 187 2 97.3 ... 9495.0 6918.0
## 109 0 114.2 ... 12440.0 16695.0
## 52 1 93.1 ... 6795.0 6095.0
## 111 0 107.9 ... 15580.0 16630.0
## 80 3 96.3 ... 9959.0 9279.0
## 103 0 100.4 ... 13499.0 13499.0
## 75 1 102.7 ... 16503.0 18420.0
## 10 2 101.2 ... 16430.0 13950.0
## 54 1 93.1 ... 7395.0 6695.0
## 40 0 96.5 ... 10295.0 8845.0
## 57 3 95.3 ... 13645.0 11395.0
## 66 0 104.9 ... 18344.0 11048.0
## 137 2 99.1 ... 18620.0 18150.0
## 161 0 95.7 ... 8358.0 7198.0
## 158 0 95.7 ... 7898.0 7788.0
## 11 0 101.2 ... 16925.0 13950.0
## 171 2 98.4 ... 11549.0 17669.0
## 2 1 94.5 ... 16500.0 14869.0
## 177 -1 102.4 ... 11248.0 9988.0
## 184 2 97.3 ... 7995.0 7775.0
## 21 1 93.7 ... 5572.0 5572.0
## 82 3 95.9 ... 12629.0 14869.0
## 125 3 94.5 ... 22018.0 12964.0
## 6 1 105.8 ... 17710.0 16845.0
## 65 0 104.9 ... 18280.0 24565.0
## 48 0 113.0 ... 35550.0 32250.0
## 42 1 96.5 ... 10345.0 9988.0
## 27 1 93.7 ... 8558.0 7957.0
## 79 1 93.0 ... 7689.0 7957.0
##
## [41 rows x 16 columns]3.6.2.5 RMSE modelo de ar
rmse_ar = mean_squared_error(
y_true = Y_valida,
y_pred = predicciones_ar,
squared = False
)
print(f"El error (rmse) de test es: {rmse_ar}")## El error (rmse) de test es: 3122.9086855905o
print('Root Mean Squared Error RMSE:', np.sqrt(metrics.mean_squared_error(Y_valida, predicciones_ar)))## Root Mean Squared Error RMSE: 3122.90868559053.6.2.6 Modelo de bosques aleatorios (RF)
Se construye el modelo de árbol de regresión (ar). Semilla 1349 y 20 árboles de entrenamiento
modelo_rf = RandomForestRegressor(n_estimators = 20, random_state = 1349)
modelo_rf.fit(X_entrena, Y_entrena)RandomForestRegressor(n_estimators=20, random_state=1349)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestRegressor(n_estimators=20, random_state=1349)
3.6.2.7 Variables de importancia
# pendiente ... ...3.6.2.8 Predicciones del modelo (rf)
predicciones_rf = modelo_rf.predict(X_valida)
predicciones_rf## array([ 7647.6 , 10356.75 , 18849.95 , 16501.4 ,
## 8928.81666667, 8420.05 , 14567.55835 , 21848.5 ,
## 10358.4 , 33807.5 , 33807.5 , 40826.1 ,
## 8407.35 , 17151. , 6008.7 , 15583.6 ,
## 10601.25 , 16168.55 , 17880.8167 , 10670.4 ,
## 6672.7 , 8652. , 11317.15 , 12503.2 ,
## 17130.50835 , 7990.1 , 7881. , 10098.75 ,
## 13202.6 , 15045.77501667, 10447.85 , 8324.3 ,
## 5983.89166667, 14567.55835 , 15538.00835 , 21848.5 ,
## 16832.725 , 32259.2 , 9695.95 , 8033.89166667,
## 8051.59166667])3.6.2.9 Tabla comparativa
comparaciones = pd.DataFrame(X_valida)
comparaciones = comparaciones.assign(Precio_Real = Y_valida)
comparaciones = comparaciones.assign(Precio_Prediccion = predicciones_rf.flatten().tolist())
print(comparaciones)## symboling wheelbase ... Precio_Real Precio_Prediccion
## 154 0 95.7 ... 7898.0 7647.600000
## 147 0 97.0 ... 10198.0 10356.750000
## 104 3 91.3 ... 17199.0 18849.950000
## 102 0 100.4 ... 14399.0 16501.400000
## 61 1 98.8 ... 10595.0 8928.816667
## 163 1 94.5 ... 8058.0 8420.050000
## 124 3 95.9 ... 12764.0 14567.558350
## 7 1 105.8 ... 18920.0 21848.500000
## 169 2 98.4 ... 9989.0 10358.400000
## 16 0 103.5 ... 41315.0 33807.500000
## 15 0 103.5 ... 30760.0 33807.500000
## 73 0 120.9 ... 40960.0 40826.100000
## 187 2 97.3 ... 9495.0 8407.350000
## 109 0 114.2 ... 12440.0 17151.000000
## 52 1 93.1 ... 6795.0 6008.700000
## 111 0 107.9 ... 15580.0 15583.600000
## 80 3 96.3 ... 9959.0 10601.250000
## 103 0 100.4 ... 13499.0 16168.550000
## 75 1 102.7 ... 16503.0 17880.816700
## 10 2 101.2 ... 16430.0 10670.400000
## 54 1 93.1 ... 7395.0 6672.700000
## 40 0 96.5 ... 10295.0 8652.000000
## 57 3 95.3 ... 13645.0 11317.150000
## 66 0 104.9 ... 18344.0 12503.200000
## 137 2 99.1 ... 18620.0 17130.508350
## 161 0 95.7 ... 8358.0 7990.100000
## 158 0 95.7 ... 7898.0 7881.000000
## 11 0 101.2 ... 16925.0 10098.750000
## 171 2 98.4 ... 11549.0 13202.600000
## 2 1 94.5 ... 16500.0 15045.775017
## 177 -1 102.4 ... 11248.0 10447.850000
## 184 2 97.3 ... 7995.0 8324.300000
## 21 1 93.7 ... 5572.0 5983.891667
## 82 3 95.9 ... 12629.0 14567.558350
## 125 3 94.5 ... 22018.0 15538.008350
## 6 1 105.8 ... 17710.0 21848.500000
## 65 0 104.9 ... 18280.0 16832.725000
## 48 0 113.0 ... 35550.0 32259.200000
## 42 1 96.5 ... 10345.0 9695.950000
## 27 1 93.7 ... 8558.0 8033.891667
## 79 1 93.0 ... 7689.0 8051.591667
##
## [41 rows x 16 columns]3.6.2.10 RMSE modelo de ar
rmse_rf = mean_squared_error(
y_true = Y_valida,
y_pred = predicciones_rf,
squared = False
)
print(f"El error (rmse) de test es: {rmse_rf}")## El error (rmse) de test es: 2830.497793616688o
print('Root Mean Squared Error RMSE:', np.sqrt(metrics.mean_squared_error(Y_valida, predicciones_rf)))## Root Mean Squared Error RMSE: 2830.4977936166883.7 Evaluación de modelos
Se comparan las predicciones
comparaciones = pd.DataFrame(X_valida)
comparaciones = comparaciones.assign(Precio_Real = Y_valida)
comparaciones = comparaciones.assign(Precio_Prediccion_rm = predicciones_rm.flatten().tolist(), Precio_Prediccion_ar = predicciones_ar.flatten().tolist(), Precio_Prediccion_rf = predicciones_rf.flatten().tolist())
print(comparaciones)## symboling wheelbase ... Precio_Prediccion_ar Precio_Prediccion_rf
## 154 0 95.7 ... 6918.0 7647.600000
## 147 0 97.0 ... 9960.0 10356.750000
## 104 3 91.3 ... 19699.0 18849.950000
## 102 0 100.4 ... 18150.0 16501.400000
## 61 1 98.8 ... 8845.0 8928.816667
## 163 1 94.5 ... 8238.0 8420.050000
## 124 3 95.9 ... 14869.0 14567.558350
## 7 1 105.8 ... 16845.0 21848.500000
## 169 2 98.4 ... 11199.0 10358.400000
## 16 0 103.5 ... 36880.0 33807.500000
## 15 0 103.5 ... 36880.0 33807.500000
## 73 0 120.9 ... 45400.0 40826.100000
## 187 2 97.3 ... 6918.0 8407.350000
## 109 0 114.2 ... 16695.0 17151.000000
## 52 1 93.1 ... 6095.0 6008.700000
## 111 0 107.9 ... 16630.0 15583.600000
## 80 3 96.3 ... 9279.0 10601.250000
## 103 0 100.4 ... 13499.0 16168.550000
## 75 1 102.7 ... 18420.0 17880.816700
## 10 2 101.2 ... 13950.0 10670.400000
## 54 1 93.1 ... 6695.0 6672.700000
## 40 0 96.5 ... 8845.0 8652.000000
## 57 3 95.3 ... 11395.0 11317.150000
## 66 0 104.9 ... 11048.0 12503.200000
## 137 2 99.1 ... 18150.0 17130.508350
## 161 0 95.7 ... 7198.0 7990.100000
## 158 0 95.7 ... 7788.0 7881.000000
## 11 0 101.2 ... 13950.0 10098.750000
## 171 2 98.4 ... 17669.0 13202.600000
## 2 1 94.5 ... 14869.0 15045.775017
## 177 -1 102.4 ... 9988.0 10447.850000
## 184 2 97.3 ... 7775.0 8324.300000
## 21 1 93.7 ... 5572.0 5983.891667
## 82 3 95.9 ... 14869.0 14567.558350
## 125 3 94.5 ... 12964.0 15538.008350
## 6 1 105.8 ... 16845.0 21848.500000
## 65 0 104.9 ... 24565.0 16832.725000
## 48 0 113.0 ... 32250.0 32259.200000
## 42 1 96.5 ... 9988.0 9695.950000
## 27 1 93.7 ... 7957.0 8033.891667
## 79 1 93.0 ... 7957.0 8051.591667
##
## [41 rows x 18 columns]Se compara el RMSE.
Se crea un arreglo numpy
rmse = np.array([[rmse_rm, rmse_ar, rmse_rf]])
rmse## array([[4075.08099952, 3122.90868559, 2830.49779362]])Se construye data.frame a partir del rreglo nmpy
rmse = pd.DataFrame(rmse)
rmse.columns = ['rmse_rm', 'rmse_ar', 'rmse_rf']
rmse## rmse_rm rmse_ar rmse_rf
## 0 4075.081 3122.908686 2830.4977944 Interpretación
En el presente ejercicio se realizo una cargade datos numéricos de precios de automóviles con respecto a algunas variables numéricas mediante un enlace de Github en formato CSV. Se cargaron datos numéricos de precios de automóviles basados en algunas variables numéricas.
El modelo de regresión linea múltiple destaca el estadístico Adjusted R-squared con un valor de 0.8686632, lo que se define como que las variables independientes explican aproximadamente el 86.86% de la variable dependiente precio.
El mejor modelo conforme al estadístico raiz del error cuadrático medio (rmse) fue bosques aleatorios; se tuvo como resultado un de 4075.0809.
Se construyeron datos de entrenamiento y validación y con el porcentaje de 80% y 20% respectivamente.
El RMSE del modelo de regresión lineal es de 4075.0809.
El RMSE del modelo de árbol de regresión es de 3122.9086.
El RMSE del modelo de bosques aleatorios es de 2830.497794.
Los datos obtenidos mostrados anteriormente fueron realizados utilizando la semilla 1349