Higgs Boson Machine Learning Challenge

Machine Learning - Project 1

Tshtsh_club: Marie Anselmet, Sofia Dandjee, Héloïse Monnet

Run the project

1. Make sure that Python >= 3.7 and NumPy >= 1.16 are installed
2. Download the train and test data sets from Kaggle competition dataset, and put train.csv and test.csv into a data\ folder.
3. Go to script\ folder and run run.py. You will get submission.csv for Kaggle in the submission\ folder.

cd script
python run.py

Script files

proj1_helpers.py

• sigmoid: Sigmoid function.
• load_csv_data: Loads data from a csv file.
• compute_f1_score, predict_accuracy: Computes the accuracy and the F1 score of a prediction.
• predict_labels: Generates class predictions for a linear or a logistic regression.
• build_k_indices, cross_validation : Generate the training and validation data for cross-validation.
• classify: Converts the (-1,1) of a label vector into (0,1), to use for the logistic regression.
• batch_iter: Generates a mini-batch for a dataset.
• create_csv_submission: Creates a csv output file for submission to Kaggle.

cost.py

• compute_loss: Computes the loss by mse for linear regression.
• logistic_loss: Compute the loss by negative log likelihood for the logistic regression.
• reg_logistic_loss : Compute the regularized logistic loss by negative log likelihood.

compute_gradient.py

• compute_gradient: Computes the gradient for the linear gradient descent.
• logistic_gradient: Compute the gradient for the logistic gradient descent.
• reg_logistic_gradient: Compute the gradient for the regularized logistic gradient descent.

data_helpers.py

• get_jet_samples: Divides the input data depending of their jet values.
• clean_data, standardize : Standardizes data, removes undefined values and features with a null standard deviation.
• augment_data, build_model_data,build_poly_all_features: Augment the data by building polynomial features.

implementations.py

• least_squares_GD: Linear regression using gradient descent.
• least_squares_SGD: Linear regression using stochastic gradient descent.
• least_squares: Least squares regression using normal equations.
• ridge_regression: Ridge regression using normal equations.
• logistic_regression: Logistic regression using stochastic gradient.
• reg_logistic_regression: Regularized logistic regression using stochastic gradient descent.

run.py

Script to produce the same .csv predictions used in the best submission on the Kaggle platform.