1. preProcess(), train(), predict() functions

  1. The caret package contains preProcess(), predict(), train() functions.

  1. It applies different transformations to the data

    • The standardization is the process to standardize your data so that the data will have a mean of 0 and a standard deviation of 1.
    • Data values can be scaled into the range of [0, 1], which is called normalization.

    1. Standalone, Training

    • BoxCox
    • YeoJohnson
    • expoTrans
    • zv
    • nzv
    • center
    • scale
    • range
    • pca
    • ica
    • spatialSign

  1. I don’t think there is a best method, different methods work best on different cases.

    • BoxCox: apply a Box-Cox transfrom, values must be non-zero and postive.
    • YeoJohnson: apply a Yeo-Johnson transform, like a BoxCox, but values can be negative.
    • expoTrans: apply a power transform like BoxCox and YeoJohnson.
    • zv: remove attributes with a zerio variance (all the same value).
    • nzv: remove attributes with a near zero variance (close to the same value).
    • center: subtract mean from values.
    • scale: divide values by standard deviation.
    • range: normalize values
    • pca: transfrom data to the principal components.
    • ica: transform data to the independent components.
    • spatialSign: project data onto a unit circle.

  1. The data transforms presented are more likely to be useful for algorithms such as regression algorithms, instance-based methods (like KNN and LVQ), support vector machines and neural networks

  1. The scale transform calculates the standard deviation for an attribute and divides each value by that standard deviation.

  1. The center transform calculates the mean for an attribute and subtracts it from each value.

  1. A Gaussian-like distribution refers to a probability distribution that exhibits a shape similar to the Gaussian distribution, also known as the normal distribution. Here are the key characteristics of a Gaussian-like distribution:

    Bell-shaped curve: The distribution is characterized by a symmetric bell-shaped curve when plotted. This means that most of the data points cluster around the mean, with fewer points appearing further away from the mean in either direction.

    Mean and variance: Like the Gaussian distribution, a Gaussian-like distribution typically has a well-defined mean (average) and variance (spread or dispersion of data points around the mean).

    Central Limit Theorem applicability: Often, distributions of real-world data approximate a Gaussian-like shape, especially when the data is the result of the sum of many independent, identically distributed random variables, as described by the Central Limit Theorem.

    Skewness and kurtosis: Gaussian-like distributions tend to have skewness close to zero (symmetry around the mean) and kurtosis near 3 (similar to the normal distribution). 

    library(dplyr)
## Warning: package 'dplyr' was built under R version 4.4.1
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
    data(iris)
    head(iris, 5)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
    dim(iris)
## [1] 150   5
    summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 
    library(mlbench)
## Warning: package 'mlbench' was built under R version 4.4.1
    data(PimaIndiansDiabetes)
    head(PimaIndiansDiabetes, 5)
##   pregnant glucose pressure triceps insulin mass pedigree age diabetes
## 1        6     148       72      35       0 33.6    0.627  50      pos
## 2        1      85       66      29       0 26.6    0.351  31      neg
## 3        8     183       64       0       0 23.3    0.672  32      pos
## 4        1      89       66      23      94 28.1    0.167  21      neg
## 5        0     137       40      35     168 43.1    2.288  33      pos
    dim(PimaIndiansDiabetes)
## [1] 768   9
    summary(PimaIndiansDiabetes)
##     pregnant         glucose         pressure         triceps     
##  Min.   : 0.000   Min.   :  0.0   Min.   :  0.00   Min.   : 0.00  
##  1st Qu.: 1.000   1st Qu.: 99.0   1st Qu.: 62.00   1st Qu.: 0.00  
##  Median : 3.000   Median :117.0   Median : 72.00   Median :23.00  
##  Mean   : 3.845   Mean   :120.9   Mean   : 69.11   Mean   :20.54  
##  3rd Qu.: 6.000   3rd Qu.:140.2   3rd Qu.: 80.00   3rd Qu.:32.00  
##  Max.   :17.000   Max.   :199.0   Max.   :122.00   Max.   :99.00  
##     insulin           mass          pedigree           age        diabetes 
##  Min.   :  0.0   Min.   : 0.00   Min.   :0.0780   Min.   :21.00   neg:500  
##  1st Qu.:  0.0   1st Qu.:27.30   1st Qu.:0.2437   1st Qu.:24.00   pos:268  
##  Median : 30.5   Median :32.00   Median :0.3725   Median :29.00            
##  Mean   : 79.8   Mean   :31.99   Mean   :0.4719   Mean   :33.24            
##  3rd Qu.:127.2   3rd Qu.:36.60   3rd Qu.:0.6262   3rd Qu.:41.00            
##  Max.   :846.0   Max.   :67.10   Max.   :2.4200   Max.   :81.00
There are 150 rows in Iris and 768 rows in Pima Indian Diabetes.
The dataset Iris contains a set of 150 records under five attributes: sepal length, sepal width, petal length, petal width and species
The dataset Pima Indian Diabetes consists of several medical predictor variables and one target variable, diabetes. Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and so on. It has 768 rows and 9 columns.


  1. The PCA transforms the data to return only the principal components, a technique from multivariate statistics and linear algebra.

    Transform the data to the independent components. The ICA retains those components that are independent. You must specify the number of desired independent components with the n.comp argument.
  1. Pre-processing refers to the steps and techniques applied to raw data before it can be analyzed or used in machine learning algorithms. It’s a crucial stage in data science and machine learning pipelines because the quality and effectiveness of the final model often depend on the quality of the pre-processed data. Here’s a breakdown of the typical steps involved in pre-processing:

Data Cleaning: Handling missing data: This involves strategies such as imputation (replacing missing values with estimated ones) or removal of incomplete records. Handling noisy data: Outliers or errors in the data can be identified and either corrected or removed if they significantly affect the analysis.

Data Integration:

Combining data from multiple sources if necessary, ensuring compatibility in terms of formats and resolving any inconsistencies.

Data Transformation:

Normalization: Scaling numeric data to a standard range, like between 0 and 1, to avoid features with larger ranges dominating those with smaller ranges. Standardization: Transforming data to have a mean of 0 and a standard deviation of 1, which can be important for algorithms that assume normally distributed data. Encoding categorical variables: Converting categorical data into a numerical format suitable for machine learning algorithms. This can involve techniques like one-hot encoding or label encoding.

Feature Selection:

Choosing the most relevant features (variables) to include in the model, while excluding irrelevant or redundant ones. This helps in improving model performance, reducing overfitting, and speeding up training.

Data Reduction:

Dimensionality reduction techniques like Principal Component Analysis (PCA) or feature extraction methods to reduce the number of variables under consideration. This can simplify the model and improve computational efficiency.

Data Discretization:

Converting continuous data into discrete categories, which can sometimes be beneficial for certain types of analyses or algorithms.

Normalization:

Adjusting data to ensure that all features have a similar scale and distribution, which can help improve the performance and convergence of machine learning algorithms.

Each of these steps aims to prepare the data in a way that optimizes the performance and accuracy of the machine learning model being developed. The specific pre-processing steps applied depend heavily on the characteristics of the data, the requirements of the model, and the nature of the problem being solved.