61 lines
4.2 KiB
Markdown
61 lines
4.2 KiB
Markdown
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# Description of Assignment 4
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### Author: [Fotios Lygerakis](https://github.com/ligerfotis)
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### Implement the Perceptron algorithm for classification of the Iris dataset.
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* Use load_iris() from sklearn.datasets to load the Iris dataset.
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* The Iris dataset is a multiclass classification dataset with 3 classes.
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* The Iris dataset has 4 features and 150 samples.
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* The Iris dataset is a balanced dataset with 50 samples per class.
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* Split the dataset into train and test sets.
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* Scale the features.
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* Implement the Perceptron algorithm for classification.
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* Use the unit step function as the activation function.
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* Evaluate the model on the test set.
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* Use accuracy_score() from sklearn.metrics to calculate the accuracy of the model.
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* Do some hyperparameter tuning to improve the accuracy of the model.
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* Try different values of the learning rate and the number of iterations.
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* Print the accuracy of the model for different values of the learning rate and the number of iterations.
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### Implement Non-linear feature transformation for regression of the Concrete Compressive Strength dataset.
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* Use read_excel() from pandas to load the dataset.
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* The Concrete Compressive Strength dataset
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* is a regression dataset with 1 target variable.
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* has 8 features and 1030 samples.
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* has 1 target variable.
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* Split the dataset into train and test sets.
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* Scale the features.
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* **Polynomial feature transformation**
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* Implement the polynomial_features() function to transform the features.
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* Use LinearRegression() from sklearn.linear_model to train a linear regression model.
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* Evaluate the model on the test set.
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* Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the model.
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* Use r2_score() from sklearn.metrics to calculate the R2 score of the model.
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* Train a linear regression model on the polynomial features varying the degree of the polynomial from 1 to 4.
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* Evaluate the models trained on the polynomial features on the test set and compare the mean squared error of the models.
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* Discuss the results in the report.
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* **Radial Basis Function (RBF) feature transformation**
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* Implement the rbf_features() function to transform the features.
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* Use LinearRegression() from sklearn.linear_model to train a linear regression model.
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* Evaluate the model on the test set.
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* Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the model.
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* Use r2_score() from sklearn.metrics to calculate the R2 score of the model.
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* Train a linear regression model on the RBF features varying the gamma parameter from 0.1 to 10.
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* Evaluate the models trained on the RBF features on the test set and compare the mean squared error of the models.
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* Discuss the results in the report.
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### **(Bonus)** Implement the Multilayer Perceptron algorithm (2 layers) for regression of the Concrete Compressive Strength dataset.
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* Use train_test_split() from sklearn.model_selection to split the dataset into train and test sets.
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* Use StandardScaler() from sklearn.preprocessing to scale the features.
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* Implement the Multilayer Perceptron algorithm for regression.
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* The Multilayer Perceptron algorithm is a neural network with 2 layers.
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* Implement the forward propagation algorithm.
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* Implement the backward propagation algorithm.
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* Use the tanh function as the activation function for the hidden layer.
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* Use the identity function as the activation function for the output layer.
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* Evaluate the model on the test set.
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* Do some hyperparameter tuning to improve the accuracy of the model.
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* Try different values of the learning rate and the number of epochs.
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* Plot the mean squared error of the model for different values of the learning rate and the number of epochs.
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* Compare the performance of the Multilayer Perceptron algorithm with the Linear Regression algorithm.
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* Use LinearRegression() from sklearn.linear_model to train a linear regression model.
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* Use mean_squared_error() from sklearn.metrics to calculate the mean squared error of the linear regression model.
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* Compare the mean squared error of the linear regression model with the mean squared error of the multilayer perceptron model.
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