goodboychan
Hyperparameters and Parameters | Chan`s Jupyter
Hyperparameters and Parameters | Chan`s Jupyter
In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices. This is the Summary of lecture “Hyperparameter Tuning in Python”, via datacamp.
new version comes with changes in the library, anyone who find the similar problem with knn data type parsing scheme, pls change it to the code below in order to run sucessfully.
Fit each to the training data & produce predictions
knn_5_predictions = knn_5.fit(X_train.values, y_train.values).predict(X_test.values) knn_10_predictions = knn_10.fit(X_train.values, y_train.values).predict(X_test.values) knn_20_predictions = knn_20.fit(X_train.values, y_train.values).predict(X_test.values)
