1 |
Use ColumnTransformer to apply different preprocessing to different columns |
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2 |
Seven ways to select columns using ColumnTransformer |
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3 |
What is the difference between "fit" and "transform"? |
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4 |
Use "fit_transform" on training data, but "transform" (only) on testing/new data |
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5 |
Four reasons to use scikit-learn (not pandas) for ML preprocessing |
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6 |
Encode categorical features using OneHotEncoder or OrdinalEncoder |
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7 |
Handle unknown categories with OneHotEncoder by encoding them as zeros |
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8 |
Use Pipeline to chain together multiple steps |
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9 |
Add a missing indicator to encode "missingness" as a feature |
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10 |
Set a "random_state" to make your code reproducible |
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11 |
Impute missing values using KNNImputer or IterativeImputer |
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12 |
What is the difference between Pipeline and make_pipeline ? |
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13 |
Examine the intermediate steps in a Pipeline |
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14 |
HistGradientBoostingClassifier natively supports missing values |
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15 |
Three reasons not to use drop='first' with OneHotEncoder |
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16 |
Use cross_val_score and GridSearchCV on a Pipeline |
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17 |
Try RandomizedSearchCV if GridSearchCV is taking too long |
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18 |
Display GridSearchCV or RandomizedSearchCV results in a DataFrame |
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19 |
Important tuning parameters for LogisticRegression |
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20 |
Plot a confusion matrix |
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21 |
Compare multiple ROC curves in a single plot |
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22 |
Use the correct methods for each type of Pipeline |
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23 |
Display the intercept and coefficients for a linear model |
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24 |
Visualize a decision tree two different ways |
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25 |
Prune a decision tree to avoid overfitting |
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26 |
Use stratified sampling with train_test_split |
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27 |
Two ways to impute missing values for a categorical feature |
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28 |
Save a model or Pipeline using joblib |
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29 |
Vectorize two text columns in a ColumnTransformer |
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30 |
Four ways to examine the steps of a Pipeline |
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31 |
Shuffle your dataset when using cross_val_score |
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32 |
Use AUC to evaluate multiclass problems |
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33 |
Use FunctionTransformer to convert functions into transformers |
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34 |
Add feature selection to a Pipeline |
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35 |
Don't use .values when passing a pandas object to scikit-learn |
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36 |
Most parameters should be passed as keyword arguments |
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37 |
Create an interactive diagram of a Pipeline in Jupyter |
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38 |
Get the feature names output by a ColumnTransformer |
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39 |
Load a toy dataset into a DataFrame |
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40 |
Estimators only print parameters that have been changed |
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41 |
Drop the first category from binary features (only) with OneHotEncoder |
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42 |
Passthrough some columns and drop others in a ColumnTransformer |
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43 |
Use OrdinalEncoder instead of OneHotEncoder with tree-based models |
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44 |
Speed up GridSearchCV using parallel processing |
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45 |
Create feature interactions using PolynomialFeatures |
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46 |
Ensemble multiple models using VotingClassifer or VotingRegressor |
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47 |
Tune the parameters of a VotingClassifer or VotingRegressor |
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48 |
Access part of a Pipeline using slicing |
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49 |
Tune multiple models simultaneously with GridSearchCV |
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50 |
Adapt this pattern to solve many Machine Learning problems |
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