🤖⚡ scikit-learn tips

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List of all tips

Click to discuss the tip on LinkedIn, click to view the Jupyter notebook for a tip, or click to watch the tip video on YouTube:

#	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
49	Tune multiple models simultaneously with GridSearchCV
50	Adapt this pattern to solve many Machine Learning problems

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)

👉 Find out which course is right for you! 👈

Do you have any other tips?

Yes! In 2019, I posted 100 pandas tricks. I also created a video featuring my top 25 pandas tricks.

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