{"id":24290,"date":"2019-11-08T09:50:06","date_gmt":"2019-11-08T14:50:06","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=24290"},"modified":"2022-11-21T09:44:34","modified_gmt":"2022-11-21T14:44:34","slug":"scikit-learn-tutorial-part-iii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/scikit-learn-tutorial-part-iii\/","title":{"rendered":"Scikit Learn Tutorial – Part III"},"content":{"rendered":"\n

Get up-to-speed with <\/em>Part I<\/em><\/a> and <\/em>Part II<\/em><\/a>.<\/em><\/p>\n\n\n\n

Splitting the data into training and test sets <\/strong><\/p>\n\n\n\n

It is always a good idea to split the data into training and test sets before we begin the modelling process to avoid the problem of overfitting<\/strong>. Overfitting occurs when the model has high predictive power for datapoints on which it is trained but generalizes poorly on out of sample or new data.<\/p>\n\n\n\n

When we train a model using only the training set, we still have the test set to check the performance of our model. Thus, splitting of data helps us to evaluate the out of sample accuracy of our data as the test dataset is new or unseen by the model.<\/p>\n\n\n\n

We can easily implement this split using the train_test_split() function from the model selection submodule of scikit learn:<\/p>\n\n\n\n

In [9]:<\/p>\n\n\n\n

#importing the train_test_split function from the model selection submodule of scikit learn library
\nfrom sklearn.model_selection import train_test_split
\n#Splitting X into Xtrain and Xtest while splitting y into ytrain and ytest
\nX_train, X_test, y_train, y_test = train_test_split(X, y,test_size=0.25, random_state=123, stratify=y)<\/p>\n\n\n\n

We have set the value of test_size parameter in this function to 0.25. This means that we are keeping 25% of the data in the test set and will use the remaining 75% as the training set on which the model will be trained. We have also set an arbitrary “random state” (or seed) so that we can reproduce our results later on. Finally, we have stratified the sample by the target variable. This ensures our training set looks similar to the test set, making evaluation metrics more reliable.<\/p>\n\n\n\n

Next, we will be using the datasets we created to illustrate the steps in model development in scikit learn.<\/p>\n\n\n\n

Stay tuned for the next installment in which the author will discuss Steps for building a classification model with Scikit learn<\/em>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Overfitting occurs when the model has high predictive power for datapoints on which it is trained but generalizes poorly on out of sample or new data.<\/p>\n","protected":false},"author":295,"featured_media":21196,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[806,852,595,4412],"contributors-categories":[13654],"class_list":{"0":"post-24290","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-asia-pacific","12":"category-quant-development","13":"category-quant-regions","14":"tag-data-science","15":"tag-machine-learning","16":"tag-python","17":"tag-scikit-learn","18":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nScikit Learn Tutorial – Part III | IBKR Quant<\/title>\n<meta name=\"description\" content=\"Overfitting - when the model has high predictive power for datapoints on which it is trained. 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