scikit-learn-tips

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List of all tips

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

You can interact with all of these notebooks online using Binder:

Note: Some of the tips do not include any code, and can only be viewed on LinkedIn.

Who creates these tips?

Hi! I'm Kevin Markham, the founder of Data School. I've been teaching data science in Python since 2014. I create these tips because I love using scikit-learn and I want to help others use it more effectively.

How can I get better at scikit-learn?

I teach three courses:

Course 1: Introduction to Machine Learning in Python with scikit-learn (4 hours, free)
Course 2: Building an Effective Machine Learning Workflow with scikit-learn (8 hours, paid)
Course 3: Machine Learning with Text in Python (14 hours, paid)

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