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🤖 Daily scikit-learn tips
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README.md

🤖 Daily scikit-learn tips

New tips are posted on LinkedIn, Twitter, and Facebook every weekday!

👉 Sign up to receive 5 tips by email every week 👈

List of all tips

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

# 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

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 learn scikit-learn from scratch?

Watch my free video series, Introduction to Machine Learning in Python with scikit-learn. There are 10 videos totaling 4.5 hours, and each video has a corresponding Jupyter notebook. Here's the detailed list of topics that I cover in the series.

Due to changes in the scikit-learn API, a small percentage of the code shown in the videos is out-of-date. However, the code in the Jupyter notebooks is all up-to-date.

How can I get better at scikit-learn?

Take my online course, Machine Learning with Text in Python. It includes 14 hours of video lessons, detailed lesson notebooks, homework assignments with included solutions, access to a Slack team, and more. Here's the detailed list of topics that I cover in the course.

The course is not free, but you can preview a small portion of the course by watching my PyCon 2016 tutorial.

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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