Here are some auto-generated descriptions of some of the software used. train_test_split, mean_squared_error, DecisionTreeRegressor, and GridSearchCV are from sklearn, the rest was part of this project.
shuffle_split_data(X, y[, test_size, ...]) |
Shuffles and splits data into training and testing subsets |
train_test_split(*arrays, **options) |
Split arrays or matrices into random train and test subsets |
performance_metric(y_true, y_predict) |
Calculates total error between true and predicted values |
mean_squared_error(y_true, y_pred[, ...]) |
Mean squared error regression loss |
fit_model(X, y[, k, n_jobs]) |
Tunes a decision tree regressor model using GridSearchCV |
DecisionTreeRegressor([criterion, splitter, ...]) |
A decision tree regressor. |
GridSearchCV(estimator, param_grid[, ...]) |
Exhaustive search over specified parameter values for an estimator. |
learning_curves(X_train, y_train, X_test, y_test) |
Calculates performance of several models with varying training data sizes |
model_complexity(X_train, y_train, X_test, ...) |
Calculates the performance of the model as model complexity increases. |