Linear Regression with sklearn – cheat sheet

Data Science & SQL

# import and instantiate model from sklearn.linear_model import LinearRegression model = LinearRegression() #prepare test data features_train = df_train.loc[:, ‘feature_name’] target_train = df_train.loc[:, ‘target_name’] #fit (train) model and print coefficient and intercept model.fit(features_train , target_train ) print(model.coef_) print(model.intercept_) # calculate model quality from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score target_prediction = model.predict(features_train) print(mean_squared_error(target_train , target_prediction))…

New Blog Post

Confusion Matrix

Data Science & SQL

Too confused of the confusion matrix? Let me bring some clarity into this topic! Let’s take the example from Precision and Recall: y_true = [“dog”, “dog”, “non-dog”, “non-dog”, “dog”, “dog”] y_pred = [“dog”, “non-dog”, “dog”, “non-dog”, “dog”, “non-dog”] When we look at the prediction we can count the correct and incorrect classifications: dog correctly classified…