ML_course/assignment 5/README.md

[# Description of Assignment 4

Author: Fotios Lygerakis

Implement the Perceptron algorithm for classification of the Iris dataset.

• Use load_iris() from sklearn.datasets to load the Iris dataset.
  • The Iris dataset is a multiclass classification dataset with 3 classes.
  • The Iris dataset has 4 features and 150 samples.
  • The Iris dataset is a balanced dataset with 50 samples per class.
• Split the dataset into train and test sets.
• Scale the features.
• Implement the Perceptron algorithm for classification.
  • Use the unit step function as the activation function.
• Evaluate the model on the test set.
  • Use accuracy_score() from sklearn.metrics to calculate the accuracy of the model.
• Do some hyperparameter tuning to improve the accuracy of the model.
  • Try different values of the learning rate and the number of iterations.
  • Print the accuracy of the model for different values of the learning rate and the number of iterations.

Implement Non-linear feature transformation for regression of the Concrete Compressive Strength dataset.

• Use read_excel() from pandas to load the dataset.
• The Concrete Compressive Strength dataset
  • is a regression dataset with 1 target variable.
  • has 8 features and 1030 samples.
  • has 1 target variable.
• Split the dataset into train and test sets.
• Scale the features.
• Polynomial feature transformation
  • Implement the polynomial_features() function to transform the features.
  • Use LinearRegression() from sklearn.linear_model to train a linear regression model.
  • Evaluate the model on the test set.
    • Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the model.
    • Use r2_score() from sklearn.metrics to calculate the R2 score of the model.
  • Train a linear regression model on the polynomial features varying the degree of the polynomial from 1 to 4.
  • Evaluate the models trained on the polynomial features on the test set and compare the mean squared error of the models.
  • Discuss the results in the report.
• Radial Basis Function (RBF) feature transformation
  • Implement the rbf_features() function to transform the features.
  • Use LinearRegression() from sklearn.linear_model to train a linear regression model.
  • Evaluate the model on the test set.
    • Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the model.
    • Use r2_score() from sklearn.metrics to calculate the R2 score of the model.
  • Train a linear regression model on the RBF features varying the gamma parameter from 0.1 to 10.
  • Evaluate the models trained on the RBF features on the test set and compare the mean squared error of the models.
  • Discuss the results in the report.

(Bonus) Implement the Multilayer Perceptron algorithm (2 layers) for regression of the Concrete Compressive Strength dataset.

• Use train_test_split() from sklearn.model_selection to split the dataset into train and test sets.
• Use StandardScaler() from sklearn.preprocessing to scale the features.
• Implement the Multilayer Perceptron algorithm for regression.
  • The Multilayer Perceptron algorithm is a neural network with 2 layers.
  • Implement the forward propagation algorithm.
  • Implement the backward propagation algorithm.
  • Use the tanh function as the activation function for the hidden layer.
  • Use the identity function as the activation function for the output layer.
• Evaluate the model on the test set.
• Do some hyperparameter tuning to improve the accuracy of the model.
  • Try different values of the learning rate and the number of epochs.
  • Plot the mean squared error of the model for different values of the learning rate and the number of epochs.
• Compare the performance of the Multilayer Perceptron algorithm with the Linear Regression algorithm.
  • Use LinearRegression() from sklearn.linear_model to train a linear regression model.
  • Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the linear regression model.
• Compare the mean squared error of the linear regression model with the mean squared error of the multilayer perceptron model. ]()