scikit-learn

[Solved] sklearn.metrics.roc_curve only shows 5 fprs, tprs, thresholds [closed]

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This might depend on the default value of the parameter drop_intermediate (default to true) of roc_curve(), which is meant for dropping suboptimal thresholds, doc here. You might prevent such behaviour by passing drop_intermediate=False, instead. Here’s an example: import numpy as np try: from sklearn.datasets import fetch_openml mnist = fetch_openml(‘mnist_784’, version=1, cache=True) mnist[“target”] = mnist[“target”].astype(np.int8) except … Read more

[Solved] How can I impute values to outlier cells based on groups? [closed]

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Using the following answer from n1k31t4 in: https://datascience.stackexchange.com/questions/37717/imputation-missing-values-other-than-using-mean-median-in-python I was able to solve my problem. df[col]=df.groupby([‘X’, ‘Y’])[col].transform(lambda x: x.median() if (np.abs(x)>3).any() else x) solved How can I impute values to outlier cells based on groups? [closed]

[Solved] Scikit-learn Custom Scoring Function 1

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The error is in above line. You forgot to close the brackets there. Change this: print(clf.predict(x_test[1:10]) to print(clf.predict(x_test[1:10])) And even after that you will get error in the line: clf.f1_score(…) The above line is wrong, it should be: f1_score(y_test, clf.predict(x_test)) solved Scikit-learn Custom Scoring Function 1

[Solved] Predicting numerical features based on string features using sk-learn

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Below is tested and fully working code of yours: data_train = pd.read_csv(r”train.csv”) data_test = pd.read_csv(r”test.csv”) columns = [‘Id’, ‘HomeTeam’, ‘AwayTeam’, ‘Full_Time_Home_Goals’] col = [‘Id’, ‘HomeTeam’, ‘AwayTeam’] data_test = data_test[col] data_train = data_train[columns] data_train = data_train.dropna() data_test = data_test.dropna() data_train[‘Full_Time_Home_Goals’] = data_train[‘Full_Time_Home_Goals’].astype(int) from sklearn import preprocessing def encode_features(df_train, df_test): features = [‘HomeTeam’, ‘AwayTeam’] df_combined = pd.concat([df_train[features], … Read more