Caso 6. Comparación modelos de regresión. Datos Precios autos. Programación Python
Alejandro Marquez
2022-10-18
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).
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
Desarrollo
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_scoreCargar 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]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: objectDiccionario 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~
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]Datos de entrenamiento y validación
Datos de entrenamiento al 80% de los datos y 20% los datos de validación. Semilla 1279
X_entrena, X_valida, Y_entrena, Y_valida = train_test_split(datos.drop(columns = "price"), datos['price'],train_size = 0.80, random_state = 1279)Datos de entrenamiento
X_entrena## symboling wheelbase carlength ... peakrpm citympg highwaympg
## 194 -2 104.3 188.8 ... 5400 23 28
## 132 3 99.1 186.6 ... 5250 21 28
## 84 3 95.9 173.2 ... 5000 19 24
## 90 1 94.5 165.3 ... 4800 45 50
## 4 2 99.4 176.6 ... 5500 18 22
## .. ... ... ... ... ... ... ...
## 40 0 96.5 175.4 ... 5800 27 33
## 86 1 96.3 172.4 ... 5000 25 32
## 60 0 98.8 177.8 ... 4800 26 32
## 155 0 95.7 169.7 ... 4800 27 32
## 167 2 98.4 176.2 ... 4800 24 30
##
## [164 rows x 14 columns]Datos de validación
X_valida## symboling wheelbase carlength ... peakrpm citympg highwaympg
## 83 3 95.9 173.2 ... 5000 19 24
## 91 1 94.5 165.3 ... 5200 31 37
## 1 3 88.6 168.8 ... 5000 21 27
## 110 0 114.2 198.9 ... 4150 25 25
## 136 3 99.1 186.6 ... 5500 19 26
## 105 3 91.3 170.7 ... 5200 17 23
## 197 -1 104.3 188.8 ... 5400 24 28
## 49 0 102.0 191.7 ... 5000 13 17
## 181 -1 104.5 187.8 ... 5200 19 24
## 140 2 93.3 157.3 ... 4400 26 31
## 198 -2 104.3 188.8 ... 5100 17 22
## 146 0 97.0 173.5 ... 4800 28 32
## 99 0 97.2 173.4 ... 5200 27 34
## 130 0 96.1 181.5 ... 5100 23 31
## 145 0 97.0 172.0 ... 4800 24 29
## 70 -1 115.6 202.6 ... 4350 22 25
## 37 0 96.5 167.5 ... 5800 27 33
## 13 0 101.2 176.8 ... 4250 21 28
## 5 2 99.8 177.3 ... 5500 19 25
## 203 -1 109.1 188.8 ... 4800 26 27
## 131 2 96.1 176.8 ... 5100 23 31
## 48 0 113.0 199.6 ... 4750 15 19
## 174 -1 102.4 175.6 ... 4500 30 33
## 41 0 96.5 175.4 ... 5800 24 28
## 6 1 105.8 192.7 ... 5500 19 25
## 196 -2 104.3 188.8 ... 5400 24 28
## 199 -1 104.3 188.8 ... 5100 17 22
## 63 0 98.8 177.8 ... 4650 36 42
## 191 0 100.4 180.2 ... 5500 19 24
## 184 2 97.3 171.7 ... 4800 37 46
## 149 0 96.9 173.6 ... 4800 23 23
## 62 0 98.8 177.8 ... 4800 26 32
## 152 1 95.7 158.7 ... 4800 31 38
## 109 0 114.2 198.9 ... 5000 19 24
## 68 -1 110.0 190.9 ... 4350 22 25
## 127 3 89.5 168.9 ... 5900 17 25
## 201 -1 109.1 188.8 ... 5300 19 25
## 61 1 98.8 177.8 ... 4800 26 32
## 10 2 101.2 176.8 ... 5800 23 29
## 87 1 96.3 172.4 ... 5500 23 30
## 32 1 93.7 150.0 ... 5500 38 42
##
## [41 rows x 14 columns]Modelos Supervisados
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()
Coeficientes
Solo se muestran los coeficientes de: \(\beta_1, \beta_2, ...\beta_n\)
modelo_rm.coef_## array([ 1.53154482e+02, 1.11888397e+02, -7.30998253e+01, 6.43581473e+02,
## 1.10897998e+02, 1.03717880e+00, 1.29981002e+02, -1.85797897e+03,
## -3.53332377e+03, 2.98102781e+02, 2.85527772e+01, 2.38908193e+00,
## -2.81088636e+02, 1.88537018e+02])- 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))## 0.8507340734785982Predicciones del modelo rm
predicciones_rm = modelo_rm.predict(X_valida)
print(predicciones_rm[:-1])## [15160.84661158 6366.79565534 13349.3148034 17386.46742585
## 16136.16698363 24321.10020209 16232.39865749 48725.2932995
## 19737.15751738 8559.22788284 15699.67544259 9193.25186618
## 10376.97681821 10796.86845542 10674.48875487 25427.77293285
## 10209.25853887 16597.24930195 15636.34520052 20502.30050282
## 10866.4598746 31170.42354412 11685.13736124 10286.87474132
## 18948.91335475 15824.09864632 16113.16134776 12669.6279701
## 15764.31318033 10446.34934839 9907.95055504 10287.08588237
## 6323.76138793 14461.27636072 25000.86342357 27375.54611131
## 19260.52703775 10214.69449666 12964.60690436 10733.53208554]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
## 83 3 95.9 ... 14869.0 15160.846612
## 91 1 94.5 ... 6649.0 6366.795655
## 1 3 88.6 ... 16500.0 13349.314803
## 110 0 114.2 ... 13860.0 17386.467426
## 136 3 99.1 ... 18150.0 16136.166984
## 105 3 91.3 ... 19699.0 24321.100202
## 197 -1 104.3 ... 16515.0 16232.398657
## 49 0 102.0 ... 36000.0 48725.293299
## 181 -1 104.5 ... 15750.0 19737.157517
## 140 2 93.3 ... 7603.0 8559.227883
## 198 -2 104.3 ... 18420.0 15699.675443
## 146 0 97.0 ... 7463.0 9193.251866
## 99 0 97.2 ... 8949.0 10376.976818
## 130 0 96.1 ... 9295.0 10796.868455
## 145 0 97.0 ... 11259.0 10674.488755
## 70 -1 115.6 ... 31600.0 25427.772933
## 37 0 96.5 ... 7895.0 10209.258539
## 13 0 101.2 ... 21105.0 16597.249302
## 5 2 99.8 ... 15250.0 15636.345201
## 203 -1 109.1 ... 22470.0 20502.300503
## 131 2 96.1 ... 9895.0 10866.459875
## 48 0 113.0 ... 35550.0 31170.423544
## 174 -1 102.4 ... 10698.0 11685.137361
## 41 0 96.5 ... 12945.0 10286.874741
## 6 1 105.8 ... 17710.0 18948.913355
## 196 -2 104.3 ... 15985.0 15824.098646
## 199 -1 104.3 ... 18950.0 16113.161348
## 63 0 98.8 ... 10795.0 12669.627970
## 191 0 100.4 ... 13295.0 15764.313180
## 184 2 97.3 ... 7995.0 10446.349348
## 149 0 96.9 ... 11694.0 9907.950555
## 62 0 98.8 ... 10245.0 10287.085882
## 152 1 95.7 ... 6488.0 6323.761388
## 109 0 114.2 ... 12440.0 14461.276361
## 68 -1 110.0 ... 28248.0 25000.863424
## 127 3 89.5 ... 34028.0 27375.546111
## 201 -1 109.1 ... 19045.0 19260.527038
## 61 1 98.8 ... 10595.0 10214.694497
## 10 2 101.2 ... 16430.0 12964.606904
## 87 1 96.3 ... 9279.0 10733.532086
## 32 1 93.7 ... 5399.0 5756.785192
##
## [41 rows x 16 columns]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: 3274.200409009657o
print('Root Mean Squared Error RMSE:', np.sqrt(metrics.mean_squared_error(Y_valida, predicciones_rm)))## Root Mean Squared Error RMSE: 3274.200409009657Modelo de árbol de regresión (AR)
Se construye el modelo de árbol de regresión (ar)
modelo_ar = DecisionTreeRegressor(
#max_depth = 3,
random_state = 2022
)Entrenar el modelo
modelo_ar.fit(X_entrena, Y_entrena)DecisionTreeRegressor(random_state=2022)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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DecisionTreeRegressor(random_state=2022)
Visualización de árbol de regresión
fig, ax = plt.subplots(figsize=(12, 5))
print(f"Profundidad del árbol: {modelo_ar.get_depth()}")## Profundidad del árbol: 16print(f"Número de nodos terminales: {modelo_ar.get_n_leaves()}")## Número de nodos terminales: 154plot = 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 <= 2557.00
## | | |--- horsepower <= 83.00
## | | | |--- wheelbase <= 94.40
## | | | | |--- highwaympg <= 39.50
## | | | | | |--- stroke <= 2.50
## | | | | | | |--- value: [5118.00]
## | | | | | |--- stroke > 2.50
## | | | | | | |--- curbweight <= 1944.50
## | | | | | | | |--- highwaympg <= 32.50
## | | | | | | | | |--- value: [5195.00]
## | | | | | | | |--- highwaympg > 32.50
## | | | | | | | | |--- wheelbase <= 89.85
## | | | | | | | | | |--- value: [6855.00]
## | | | | | | | | |--- wheelbase > 89.85
## | | | | | | | | | |--- curbweight <= 1902.50
## | | | | | | | | | | |--- curbweight <= 1888.00
## | | | | | | | | | | | |--- value: [6377.00]
## | | | | | | | | | | |--- curbweight > 1888.00
## | | | | | | | | | | | |--- value: [6095.00]
## | | | | | | | | | |--- curbweight > 1902.50
## | | | | | | | | | | |--- symboling <= 1.50
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | | | | | | |--- symboling > 1.50
## | | | | | | | | | | | |--- value: [6189.00]
## | | | | | | |--- curbweight > 1944.50
## | | | | | | | |--- enginesize <= 90.50
## | | | | | | | | |--- curbweight <= 1978.00
## | | | | | | | | | |--- value: [6229.00]
## | | | | | | | | |--- curbweight > 1978.00
## | | | | | | | | | |--- carlength <= 162.30
## | | | | | | | | | | |--- value: [7150.50]
## | | | | | | | | | |--- carlength > 162.30
## | | | | | | | | | | |--- value: [6692.00]
## | | | | | | | |--- enginesize > 90.50
## | | | | | | | | |--- carwidth <= 64.30
## | | | | | | | | | |--- compressionratio <= 9.30
## | | | | | | | | | | |--- curbweight <= 1947.50
## | | | | | | | | | | | |--- value: [6695.00]
## | | | | | | | | | | |--- curbweight > 1947.50
## | | | | | | | | | | | |--- truncated branch of depth 4
## | | | | | | | | | |--- compressionratio > 9.30
## | | | | | | | | | | |--- value: [7609.00]
## | | | | | | | | |--- carwidth > 64.30
## | | | | | | | | | |--- value: [6669.00]
## | | | | |--- highwaympg > 39.50
## | | | | | |--- compressionratio <= 9.55
## | | | | | | |--- horsepower <= 58.00
## | | | | | | | |--- value: [5151.00]
## | | | | | | |--- horsepower > 58.00
## | | | | | | | |--- enginesize <= 91.00
## | | | | | | | | |--- value: [5572.00]
## | | | | | | | |--- enginesize > 91.00
## | | | | | | | | |--- value: [5389.00]
## | | | | | |--- compressionratio > 9.55
## | | | | | | |--- value: [6479.00]
## | | | |--- wheelbase > 94.40
## | | | | |--- curbweight <= 2115.50
## | | | | | |--- carheight <= 54.00
## | | | | | | |--- citympg <= 30.50
## | | | | | | | |--- carheight <= 52.90
## | | | | | | | | |--- value: [7198.00]
## | | | | | | | |--- carheight > 52.90
## | | | | | | | | |--- value: [6938.00]
## | | | | | | |--- citympg > 30.50
## | | | | | | | |--- carlength <= 157.35
## | | | | | | | | |--- symboling <= 0.50
## | | | | | | | | | |--- value: [8916.50]
## | | | | | | | | |--- symboling > 0.50
## | | | | | | | | | |--- value: [7605.75]
## | | | | | | | |--- carlength > 157.35
## | | | | | | | | |--- compressionratio <= 9.50
## | | | | | | | | | |--- carlength <= 164.00
## | | | | | | | | | | |--- value: [8249.00]
## | | | | | | | | | |--- carlength > 164.00
## | | | | | | | | | | |--- curbweight <= 2026.00
## | | | | | | | | | | | |--- value: [7349.00]
## | | | | | | | | | | |--- curbweight > 2026.00
## | | | | | | | | | | | |--- truncated branch of depth 3
## | | | | | | | | |--- compressionratio > 9.50
## | | | | | | | | | |--- value: [6575.00]
## | | | | | |--- carheight > 54.00
## | | | | | | |--- compressionratio <= 9.10
## | | | | | | | |--- curbweight <= 2012.50
## | | | | | | | | |--- value: [5348.00]
## | | | | | | | |--- curbweight > 2012.50
## | | | | | | | | |--- value: [6338.00]
## | | | | | | |--- compressionratio > 9.10
## | | | | | | | |--- curbweight <= 1913.50
## | | | | | | | | |--- value: [5499.00]
## | | | | | | | |--- curbweight > 1913.50
## | | | | | | | | |--- curbweight <= 1944.50
## | | | | | | | | | |--- value: [6849.00]
## | | | | | | | | |--- curbweight > 1944.50
## | | | | | | | | | |--- peakrpm <= 5000.00
## | | | | | | | | | | |--- value: [7099.00]
## | | | | | | | | | |--- peakrpm > 5000.00
## | | | | | | | | | | |--- curbweight <= 1990.50
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | | | | | | |--- curbweight > 1990.50
## | | | | | | | | | | | |--- value: [7295.00]
## | | | | |--- curbweight > 2115.50
## | | | | | |--- curbweight <= 2304.50
## | | | | | | |--- curbweight <= 2142.50
## | | | | | | | |--- curbweight <= 2131.00
## | | | | | | | | |--- value: [8358.00]
## | | | | | | | |--- curbweight > 2131.00
## | | | | | | | | |--- value: [9258.00]
## | | | | | | |--- curbweight > 2142.50
## | | | | | | | |--- highwaympg <= 36.50
## | | | | | | | | |--- wheelbase <= 95.10
## | | | | | | | | | |--- curbweight <= 2186.50
## | | | | | | | | | | |--- value: [8058.00]
## | | | | | | | | | |--- curbweight > 2186.50
## | | | | | | | | | | |--- value: [8238.00]
## | | | | | | | | |--- wheelbase > 95.10
## | | | | | | | | | |--- carlength <= 170.85
## | | | | | | | | | | |--- value: [7898.00]
## | | | | | | | | | |--- carlength > 170.85
## | | | | | | | | | | |--- value: [7775.00]
## | | | | | | | |--- highwaympg > 36.50
## | | | | | | | | |--- stroke <= 3.19
## | | | | | | | | | |--- enginesize <= 100.00
## | | | | | | | | | | |--- value: [6918.00]
## | | | | | | | | | |--- enginesize > 100.00
## | | | | | | | | | | |--- value: [7126.00]
## | | | | | | | | |--- stroke > 3.19
## | | | | | | | | | |--- carwidth <= 64.95
## | | | | | | | | | | |--- value: [7788.00]
## | | | | | | | | | |--- carwidth > 64.95
## | | | | | | | | | | |--- value: [7775.00]
## | | | | | |--- curbweight > 2304.50
## | | | | | | |--- carlength <= 172.80
## | | | | | | | |--- horsepower <= 75.00
## | | | | | | | | |--- value: [9495.00]
## | | | | | | | |--- horsepower > 75.00
## | | | | | | | | |--- value: [9233.00]
## | | | | | | |--- carlength > 172.80
## | | | | | | | |--- value: [8013.00]
## | | |--- horsepower > 83.00
## | | | |--- citympg <= 23.50
## | | | | |--- stroke <= 3.36
## | | | | | |--- curbweight <= 2382.50
## | | | | | | |--- value: [11395.00]
## | | | | | |--- curbweight > 2382.50
## | | | | | | |--- citympg <= 22.00
## | | | | | | | |--- horsepower <= 123.00
## | | | | | | | | |--- citympg <= 19.00
## | | | | | | | | | |--- value: [13645.00]
## | | | | | | | | |--- citympg > 19.00
## | | | | | | | | | |--- value: [13495.00]
## | | | | | | | |--- horsepower > 123.00
## | | | | | | | | |--- value: [15645.00]
## | | | | | | |--- citympg > 22.00
## | | | | | | | |--- value: [16925.00]
## | | | | |--- stroke > 3.36
## | | | | | |--- carlength <= 172.70
## | | | | | | |--- value: [9279.00]
## | | | | | |--- carlength > 172.70
## | | | | | | |--- value: [9959.00]
## | | | |--- citympg > 23.50
## | | | | |--- compressionratio <= 9.70
## | | | | | |--- curbweight <= 2216.50
## | | | | | | |--- carheight <= 50.70
## | | | | | | | |--- value: [8558.00]
## | | | | | | |--- carheight > 50.70
## | | | | | | | |--- wheelbase <= 93.35
## | | | | | | | | |--- value: [7689.00]
## | | | | | | | |--- wheelbase > 93.35
## | | | | | | | | |--- curbweight <= 2210.50
## | | | | | | | | | |--- highwaympg <= 32.00
## | | | | | | | | | | |--- value: [7957.00]
## | | | | | | | | | |--- highwaympg > 32.00
## | | | | | | | | | | |--- value: [7975.00]
## | | | | | | | | |--- curbweight > 2210.50
## | | | | | | | | | |--- value: [8195.00]
## | | | | | |--- curbweight > 2216.50
## | | | | | | |--- horsepower <= 89.00
## | | | | | | | |--- symboling <= 0.50
## | | | | | | | | |--- carlength <= 175.00
## | | | | | | | | | |--- carheight <= 56.55
## | | | | | | | | | | |--- value: [9095.00]
## | | | | | | | | | |--- carheight > 56.55
## | | | | | | | | | | |--- value: [8921.00]
## | | | | | | | | |--- carlength > 175.00
## | | | | | | | | | |--- curbweight <= 2417.50
## | | | | | | | | | | |--- carwidth <= 63.85
## | | | | | | | | | | | |--- value: [10295.00]
## | | | | | | | | | | |--- carwidth > 63.85
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | | | | | |--- curbweight > 2417.50
## | | | | | | | | | | |--- value: [11245.00]
## | | | | | | | |--- symboling > 0.50
## | | | | | | | | |--- carlength <= 172.70
## | | | | | | | | | |--- peakrpm <= 5125.00
## | | | | | | | | | | |--- curbweight <= 2385.00
## | | | | | | | | | | | |--- value: [6989.00]
## | | | | | | | | | | |--- curbweight > 2385.00
## | | | | | | | | | | | |--- value: [8189.00]
## | | | | | | | | | |--- peakrpm > 5125.00
## | | | | | | | | | | |--- value: [8495.00]
## | | | | | | | | |--- carlength > 172.70
## | | | | | | | | | |--- compressionratio <= 8.55
## | | | | | | | | | | |--- value: [8499.00]
## | | | | | | | | | |--- compressionratio > 8.55
## | | | | | | | | | | |--- value: [8845.00]
## | | | | | | |--- horsepower > 89.00
## | | | | | | | |--- carlength <= 162.50
## | | | | | | | | |--- value: [11595.00]
## | | | | | | | |--- carlength > 162.50
## | | | | | | | | |--- compressionratio <= 9.20
## | | | | | | | | | |--- highwaympg <= 33.00
## | | | | | | | | | | |--- curbweight <= 2456.50
## | | | | | | | | | | | |--- truncated branch of depth 6
## | | | | | | | | | | |--- curbweight > 2456.50
## | | | | | | | | | | | |--- value: [11248.00]
## | | | | | | | | | |--- highwaympg > 33.00
## | | | | | | | | | | |--- carwidth <= 65.85
## | | | | | | | | | | | |--- value: [9549.00]
## | | | | | | | | | | |--- carwidth > 65.85
## | | | | | | | | | | | |--- value: [8948.00]
## | | | | | | | | |--- compressionratio > 9.20
## | | | | | | | | | |--- curbweight <= 2545.50
## | | | | | | | | | | |--- curbweight <= 2538.00
## | | | | | | | | | | | |--- truncated branch of depth 3
## | | | | | | | | | | |--- curbweight > 2538.00
## | | | | | | | | | | | |--- value: [8449.00]
## | | | | | | | | | |--- curbweight > 2545.50
## | | | | | | | | | | |--- value: [9989.00]
## | | | | |--- compressionratio > 9.70
## | | | | | |--- highwaympg <= 31.00
## | | | | | | |--- value: [13950.00]
## | | | | | |--- highwaympg > 31.00
## | | | | | | |--- value: [9995.00]
## | |--- curbweight > 2557.00
## | | |--- carwidth <= 68.65
## | | | |--- peakrpm <= 4375.00
## | | | | |--- citympg <= 23.00
## | | | | | |--- citympg <= 20.50
## | | | | | | |--- value: [24565.00]
## | | | | | |--- citympg > 20.50
## | | | | | | |--- value: [20970.00]
## | | | | |--- citympg > 23.00
## | | | | | |--- compressionratio <= 21.50
## | | | | | | |--- curbweight <= 3224.50
## | | | | | | | |--- value: [13200.00]
## | | | | | | |--- curbweight > 3224.50
## | | | | | | | |--- carlength <= 192.80
## | | | | | | | | |--- value: [17425.00]
## | | | | | | | |--- carlength > 192.80
## | | | | | | | | |--- value: [17075.00]
## | | | | | |--- compressionratio > 21.50
## | | | | | | |--- value: [18344.00]
## | | | |--- peakrpm > 4375.00
## | | | | |--- highwaympg <= 27.50
## | | | | | |--- peakrpm <= 5350.00
## | | | | | | |--- horsepower <= 153.00
## | | | | | | | |--- stroke <= 3.18
## | | | | | | | | |--- enginesize <= 130.00
## | | | | | | | | | |--- curbweight <= 3180.00
## | | | | | | | | | | |--- value: [15580.00]
## | | | | | | | | | |--- curbweight > 3180.00
## | | | | | | | | | | |--- value: [16695.00]
## | | | | | | | | |--- enginesize > 130.00
## | | | | | | | | | |--- value: [18280.00]
## | | | | | | | |--- stroke > 3.18
## | | | | | | | | |--- curbweight <= 3067.50
## | | | | | | | | | |--- enginesize <= 138.00
## | | | | | | | | | | |--- value: [11900.00]
## | | | | | | | | | |--- enginesize > 138.00
## | | | | | | | | | | |--- curbweight <= 2879.50
## | | | | | | | | | | | |--- truncated branch of depth 3
## | | | | | | | | | | |--- curbweight > 2879.50
## | | | | | | | | | | | |--- truncated branch of depth 2
## | | | | | | | | |--- curbweight > 3067.50
## | | | | | | | | | |--- carlength <= 185.65
## | | | | | | | | | | |--- carlength <= 183.15
## | | | | | | | | | | | |--- value: [13499.00]
## | | | | | | | | | | |--- carlength > 183.15
## | | | | | | | | | | | |--- value: [14399.00]
## | | | | | | | | | |--- carlength > 185.65
## | | | | | | | | | | |--- value: [16630.00]
## | | | | | | |--- horsepower > 153.00
## | | | | | | | |--- carheight <= 50.85
## | | | | | | | | |--- wheelbase <= 95.25
## | | | | | | | | | |--- value: [17199.00]
## | | | | | | | | |--- wheelbase > 95.25
## | | | | | | | | | |--- value: [18399.00]
## | | | | | | | |--- carheight > 50.85
## | | | | | | | | |--- curbweight <= 2996.00
## | | | | | | | | | |--- peakrpm <= 5100.00
## | | | | | | | | | | |--- stroke <= 3.29
## | | | | | | | | | | | |--- value: [16503.00]
## | | | | | | | | | | |--- stroke > 3.29
## | | | | | | | | | | | |--- value: [16500.00]
## | | | | | | | | | |--- peakrpm > 5100.00
## | | | | | | | | | | |--- value: [16558.00]
## | | | | | | | | |--- curbweight > 2996.00
## | | | | | | | | | |--- carheight <= 53.05
## | | | | | | | | | | |--- value: [15998.00]
## | | | | | | | | | |--- carheight > 53.05
## | | | | | | | | | | |--- value: [15690.00]
## | | | | | |--- peakrpm > 5350.00
## | | | | | | |--- compressionratio <= 9.25
## | | | | | | | |--- carheight <= 55.15
## | | | | | | | | |--- carlength <= 177.40
## | | | | | | | | | |--- value: [17450.00]
## | | | | | | | | |--- carlength > 177.40
## | | | | | | | | | |--- value: [17859.17]
## | | | | | | | |--- carheight > 55.15
## | | | | | | | | |--- wheelbase <= 103.55
## | | | | | | | | | |--- value: [18620.00]
## | | | | | | | | |--- wheelbase > 103.55
## | | | | | | | | | |--- value: [18150.00]
## | | | | | | |--- compressionratio > 9.25
## | | | | | | | |--- value: [22018.00]
## | | | | |--- highwaympg > 27.50
## | | | | | |--- carwidth <= 65.40
## | | | | | | |--- carwidth <= 64.40
## | | | | | | | |--- value: [8778.00]
## | | | | | | |--- carwidth > 64.40
## | | | | | | | |--- value: [11048.00]
## | | | | | |--- carwidth > 65.40
## | | | | | | |--- curbweight <= 2736.00
## | | | | | | | |--- boreratio <= 3.10
## | | | | | | | | |--- enginesize <= 109.00
## | | | | | | | | | |--- value: [13845.00]
## | | | | | | | | |--- enginesize > 109.00
## | | | | | | | | | |--- value: [15040.00]
## | | | | | | | |--- boreratio > 3.10
## | | | | | | | | |--- stroke <= 3.45
## | | | | | | | | | |--- symboling <= 2.50
## | | | | | | | | | | |--- wheelbase <= 99.75
## | | | | | | | | | | | |--- value: [12170.00]
## | | | | | | | | | | |--- wheelbase > 99.75
## | | | | | | | | | | | |--- value: [12290.00]
## | | | | | | | | | |--- symboling > 2.50
## | | | | | | | | | | |--- value: [11850.00]
## | | | | | | | | |--- stroke > 3.45
## | | | | | | | | | |--- curbweight <= 2696.50
## | | | | | | | | | | |--- value: [11199.00]
## | | | | | | | | | |--- curbweight > 2696.50
## | | | | | | | | | | |--- value: [11549.00]
## | | | | | | |--- curbweight > 2736.00
## | | | | | | | |--- wheelbase <= 101.70
## | | | | | | | | |--- carwidth <= 66.05
## | | | | | | | | | |--- value: [17669.00]
## | | | | | | | | |--- carwidth > 66.05
## | | | | | | | | | |--- value: [15510.00]
## | | | | | | | |--- wheelbase > 101.70
## | | | | | | | | |--- symboling <= -1.50
## | | | | | | | | | |--- value: [12940.00]
## | | | | | | | | |--- symboling > -1.50
## | | | | | | | | | |--- value: [13415.00]
## | | |--- carwidth > 68.65
## | | | |--- curbweight <= 2983.00
## | | | | |--- carheight <= 55.60
## | | | | | |--- value: [16845.00]
## | | | | |--- carheight > 55.60
## | | | | | |--- value: [18920.00]
## | | | |--- curbweight > 2983.00
## | | | | |--- stroke <= 3.28
## | | | | | |--- boreratio <= 3.68
## | | | | | | |--- value: [21485.00]
## | | | | | |--- boreratio > 3.68
## | | | | | | |--- value: [22625.00]
## | | | | |--- stroke > 3.28
## | | | | | |--- value: [23875.00]
## |--- enginesize > 182.00
## | |--- highwaympg <= 17.00
## | | |--- symboling <= 0.50
## | | | |--- value: [40960.00]
## | | |--- symboling > 0.50
## | | | |--- value: [45400.00]
## | |--- highwaympg > 17.00
## | | |--- horsepower <= 139.00
## | | | |--- carlength <= 189.20
## | | | | |--- value: [28176.00]
## | | | |--- carlength > 189.20
## | | | | |--- value: [25552.00]
## | | |--- horsepower > 139.00
## | | | |--- curbweight <= 3373.00
## | | | | |--- curbweight <= 3015.00
## | | | | | |--- curbweight <= 2778.00
## | | | | | | |--- value: [32528.00]
## | | | | | |--- curbweight > 2778.00
## | | | | | | |--- value: [37028.00]
## | | | | |--- curbweight > 3015.00
## | | | | | |--- compressionratio <= 9.00
## | | | | | | |--- value: [30760.00]
## | | | | | |--- compressionratio > 9.00
## | | | | | | |--- value: [31400.50]
## | | | |--- curbweight > 3373.00
## | | | | |--- carwidth <= 68.75
## | | | | | |--- value: [41315.00]
## | | | | |--- carwidth > 68.75
## | | | | | |--- carlength <= 198.30
## | | | | | | |--- compressionratio <= 8.15
## | | | | | | | |--- value: [36880.00]
## | | | | | | |--- compressionratio > 8.15
## | | | | | | | |--- value: [35056.00]
## | | | | | |--- carlength > 198.30
## | | | | | | |--- citympg <= 15.50
## | | | | | | | |--- value: [32250.00]
## | | | | | | |--- citympg > 15.50
## | | | | | | | |--- value: [34184.00]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 0.670830
## 5 curbweight 0.206807
## 10 horsepower 0.033579
## 13 highwaympg 0.026208
## 3 carwidth 0.018517
## 12 citympg 0.013630
## 11 peakrpm 0.012132
## 8 stroke 0.005338
## 9 compressionratio 0.003770
## 1 wheelbase 0.003268
## 2 carlength 0.002260
## 0 symboling 0.001523
## 4 carheight 0.001100
## 7 boreratio 0.001040Estos sería los predictores más importantes para el modelo de árbol de regresión enginesize, curbweight, peakrpm, carheight y wheelbase
Predicciones del modelo (ar)
predicciones_ar = modelo_ar.predict(X = X_valida)
predicciones_ar## array([14489. , 6849. , 13495. , 17075. , 18620. , 17199. ,
## 13415. , 40960. , 15690. , 7053. , 15690. , 7775. ,
## 9549. , 11199. , 11248. , 25552. , 9095. , 20970. ,
## 9959. , 23875. , 9959. , 32250. , 8013. , 11248. ,
## 18920. , 12940. , 15690. , 8013. , 17859.167, 7775. ,
## 15580. , 8495. , 6338. , 16630. , 25552. , 32528. ,
## 22625. , 8845. , 16925. , 9279. , 6479. ])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
## 83 3 95.9 ... 14869.0 14489.000
## 91 1 94.5 ... 6649.0 6849.000
## 1 3 88.6 ... 16500.0 13495.000
## 110 0 114.2 ... 13860.0 17075.000
## 136 3 99.1 ... 18150.0 18620.000
## 105 3 91.3 ... 19699.0 17199.000
## 197 -1 104.3 ... 16515.0 13415.000
## 49 0 102.0 ... 36000.0 40960.000
## 181 -1 104.5 ... 15750.0 15690.000
## 140 2 93.3 ... 7603.0 7053.000
## 198 -2 104.3 ... 18420.0 15690.000
## 146 0 97.0 ... 7463.0 7775.000
## 99 0 97.2 ... 8949.0 9549.000
## 130 0 96.1 ... 9295.0 11199.000
## 145 0 97.0 ... 11259.0 11248.000
## 70 -1 115.6 ... 31600.0 25552.000
## 37 0 96.5 ... 7895.0 9095.000
## 13 0 101.2 ... 21105.0 20970.000
## 5 2 99.8 ... 15250.0 9959.000
## 203 -1 109.1 ... 22470.0 23875.000
## 131 2 96.1 ... 9895.0 9959.000
## 48 0 113.0 ... 35550.0 32250.000
## 174 -1 102.4 ... 10698.0 8013.000
## 41 0 96.5 ... 12945.0 11248.000
## 6 1 105.8 ... 17710.0 18920.000
## 196 -2 104.3 ... 15985.0 12940.000
## 199 -1 104.3 ... 18950.0 15690.000
## 63 0 98.8 ... 10795.0 8013.000
## 191 0 100.4 ... 13295.0 17859.167
## 184 2 97.3 ... 7995.0 7775.000
## 149 0 96.9 ... 11694.0 15580.000
## 62 0 98.8 ... 10245.0 8495.000
## 152 1 95.7 ... 6488.0 6338.000
## 109 0 114.2 ... 12440.0 16630.000
## 68 -1 110.0 ... 28248.0 25552.000
## 127 3 89.5 ... 34028.0 32528.000
## 201 -1 109.1 ... 19045.0 22625.000
## 61 1 98.8 ... 10595.0 8845.000
## 10 2 101.2 ... 16430.0 16925.000
## 87 1 96.3 ... 9279.0 9279.000
## 32 1 93.7 ... 5399.0 6479.000
##
## [41 rows x 16 columns]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: 2583.235362230902o
print('Root Mean Squared Error RMSE:', np.sqrt(metrics.mean_squared_error(Y_valida, predicciones_ar)))## Root Mean Squared Error RMSE: 2583.235362230902Modelo de bosques aleatorios (RF)
Se construye el modelo de árbol de regresión (ar). Semilla 2022 y 20 árboles de entrenamiento
modelo_rf = RandomForestRegressor(n_estimators = 20, random_state = 2022)
modelo_rf.fit(X_entrena, Y_entrena)RandomForestRegressor(n_estimators=20, random_state=2022)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=2022)
Variables de importancia
# pendiente ... ...Predicciones del modelo (rf)
predicciones_rf = modelo_rf.predict(X_valida)
predicciones_rf## array([14372.25 , 6870.825 , 12041.25 , 17622. , 16788.1 ,
## 17087.6 , 13827.05 , 38090.425 , 17047.05 , 7904.1 ,
## 17001. , 8493.55 , 9293.95 , 12045.45 , 10209.1 ,
## 27342.3 , 8752.85 , 18803.75 , 14565.55835, 19037.75 ,
## 11802.6 , 35166.3 , 10769.35 , 11089.05 , 20403.95835,
## 13693.45 , 17557.3 , 10204.65 , 15593.4167 , 8167.5 ,
## 12719.2 , 9141.1 , 6567. , 17678.45 , 25996.95 ,
## 34271.625 , 19811.6 , 8931.9 , 14121.05 , 9824.2 ,
## 6062.59375])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
## 83 3 95.9 ... 14869.0 14372.25000
## 91 1 94.5 ... 6649.0 6870.82500
## 1 3 88.6 ... 16500.0 12041.25000
## 110 0 114.2 ... 13860.0 17622.00000
## 136 3 99.1 ... 18150.0 16788.10000
## 105 3 91.3 ... 19699.0 17087.60000
## 197 -1 104.3 ... 16515.0 13827.05000
## 49 0 102.0 ... 36000.0 38090.42500
## 181 -1 104.5 ... 15750.0 17047.05000
## 140 2 93.3 ... 7603.0 7904.10000
## 198 -2 104.3 ... 18420.0 17001.00000
## 146 0 97.0 ... 7463.0 8493.55000
## 99 0 97.2 ... 8949.0 9293.95000
## 130 0 96.1 ... 9295.0 12045.45000
## 145 0 97.0 ... 11259.0 10209.10000
## 70 -1 115.6 ... 31600.0 27342.30000
## 37 0 96.5 ... 7895.0 8752.85000
## 13 0 101.2 ... 21105.0 18803.75000
## 5 2 99.8 ... 15250.0 14565.55835
## 203 -1 109.1 ... 22470.0 19037.75000
## 131 2 96.1 ... 9895.0 11802.60000
## 48 0 113.0 ... 35550.0 35166.30000
## 174 -1 102.4 ... 10698.0 10769.35000
## 41 0 96.5 ... 12945.0 11089.05000
## 6 1 105.8 ... 17710.0 20403.95835
## 196 -2 104.3 ... 15985.0 13693.45000
## 199 -1 104.3 ... 18950.0 17557.30000
## 63 0 98.8 ... 10795.0 10204.65000
## 191 0 100.4 ... 13295.0 15593.41670
## 184 2 97.3 ... 7995.0 8167.50000
## 149 0 96.9 ... 11694.0 12719.20000
## 62 0 98.8 ... 10245.0 9141.10000
## 152 1 95.7 ... 6488.0 6567.00000
## 109 0 114.2 ... 12440.0 17678.45000
## 68 -1 110.0 ... 28248.0 25996.95000
## 127 3 89.5 ... 34028.0 34271.62500
## 201 -1 109.1 ... 19045.0 19811.60000
## 61 1 98.8 ... 10595.0 8931.90000
## 10 2 101.2 ... 16430.0 14121.05000
## 87 1 96.3 ... 9279.0 9824.20000
## 32 1 93.7 ... 5399.0 6062.59375
##
## [41 rows x 16 columns]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: 2073.366352389405o
print('Root Mean Squared Error RMSE:', np.sqrt(metrics.mean_squared_error(Y_valida, predicciones_rf)))## Root Mean Squared Error RMSE: 2073.366352389405Evaluación 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
## 83 3 95.9 ... 14489.000 14372.25000
## 91 1 94.5 ... 6849.000 6870.82500
## 1 3 88.6 ... 13495.000 12041.25000
## 110 0 114.2 ... 17075.000 17622.00000
## 136 3 99.1 ... 18620.000 16788.10000
## 105 3 91.3 ... 17199.000 17087.60000
## 197 -1 104.3 ... 13415.000 13827.05000
## 49 0 102.0 ... 40960.000 38090.42500
## 181 -1 104.5 ... 15690.000 17047.05000
## 140 2 93.3 ... 7053.000 7904.10000
## 198 -2 104.3 ... 15690.000 17001.00000
## 146 0 97.0 ... 7775.000 8493.55000
## 99 0 97.2 ... 9549.000 9293.95000
## 130 0 96.1 ... 11199.000 12045.45000
## 145 0 97.0 ... 11248.000 10209.10000
## 70 -1 115.6 ... 25552.000 27342.30000
## 37 0 96.5 ... 9095.000 8752.85000
## 13 0 101.2 ... 20970.000 18803.75000
## 5 2 99.8 ... 9959.000 14565.55835
## 203 -1 109.1 ... 23875.000 19037.75000
## 131 2 96.1 ... 9959.000 11802.60000
## 48 0 113.0 ... 32250.000 35166.30000
## 174 -1 102.4 ... 8013.000 10769.35000
## 41 0 96.5 ... 11248.000 11089.05000
## 6 1 105.8 ... 18920.000 20403.95835
## 196 -2 104.3 ... 12940.000 13693.45000
## 199 -1 104.3 ... 15690.000 17557.30000
## 63 0 98.8 ... 8013.000 10204.65000
## 191 0 100.4 ... 17859.167 15593.41670
## 184 2 97.3 ... 7775.000 8167.50000
## 149 0 96.9 ... 15580.000 12719.20000
## 62 0 98.8 ... 8495.000 9141.10000
## 152 1 95.7 ... 6338.000 6567.00000
## 109 0 114.2 ... 16630.000 17678.45000
## 68 -1 110.0 ... 25552.000 25996.95000
## 127 3 89.5 ... 32528.000 34271.62500
## 201 -1 109.1 ... 22625.000 19811.60000
## 61 1 98.8 ... 8845.000 8931.90000
## 10 2 101.2 ... 16925.000 14121.05000
## 87 1 96.3 ... 9279.000 9824.20000
## 32 1 93.7 ... 6479.000 6062.59375
##
## [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([[3274.20040901, 2583.23536223, 2073.36635239]])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 3274.200409 2583.235362 2073.366352Interpretación
El RMSE del modelo de regresión lineal es de 3274.200409
El RMSE del modelo de árbol de regresión es de 2583.235362
El RMSE del modelo de bosques aleatorios es de 2073.366352
Con estos resultados, tomando en cuenta las cifras de RMSE de cada uno de los modelos, podemos decir que en Python el modelo más óptimo para estos datos con la semilla 1279 es el modelo de bosques aleatorios, resultado que también resulta el más óptimo si se utilizan los mismos datos y la semilla 2022.