Machine Learning
Machine Learning in R and Python
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Part 10 - Model Selection & Boosting
Section 48 - Model Selection
k-Fold Cross Validation in Python
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Kernel SVM to the Training set
Predicting the Test set results
Making the Confusion Matrix
Applying k-Fold Cross Validation
Importing the libraries
Importing the dataset
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Kernel SVM to the Training set
Predicting the Test set results
Making the Confusion Matrix
Applying k-Fold Cross Validation
Applying Grid Search to find the best model and the best parameters
Importing the dataset
Encoding the target feature as factor
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Kernel SVM to the Training set
Predicting the Test set results
Making the Confusion Matrix
Applying k-Fold Cross Validation
Importing the dataset
Encoding the target feature as factor
Splitting the dataset into the Training set and Test set
Feature Scaling
Fitting Kernel SVM to the Training set
Predicting the Test set results
Making the Confusion Matrix
Applying k-Fold Cross Validation
Applying Grid Search to find the best parameters
Section 49 - XGBoost
Importing the libraries
Importing the dataset
Encoding categorical data
Splitting the dataset into the Training set and Test set
Fitting XGBoost to the Training set
Predicting the Test set results
Making the Confusion Matrix
Applying k-Fold Cross Validation
Importing the dataset
Encoding the categorical variables as factors
Splitting the dataset into the Training set and Test set
Fitting XGBoost to the Training set
Predicting the Test set results
Making the Confusion Matrix
Applying k-Fold Cross Validation
Section 50 - Part Recap
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