Machine Learning Methods and Applications

This section includes some Machine Learning applications and reports using R.

Contents

1.Training Linear Regression Model (R)

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2.Training a Logistic Regression Model (R)

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3.Training Multinomial Regression Model (R)

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4.Using Feature Engineering Methods to Improve Model Performance (R)

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5.Performance comparison of models trained with Decision Trees and Multivariate (R)

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6.Performance comparison of models trained with Decision Trees and Linear Regression methods (R)

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7.Performance comparison of Regression Model, Regression Tree, Bagging Tree and Random Forest (R)

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8.Performance comparison of Regression Model, Regression Tree, Bagging Tree, Random Forest and Gradient Boosted Model (R)

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