justmarkham/scikit-learn-tips

🤖⚡ 50 scikit-learn tips

Jupyter Notebook

1,747

93 commits

updated Sep 5, 2022

scikit-learn.tips

See the code

README

🤖⚡ scikit-learn tips

New tips are posted on LinkedIn, Twitter, and Facebook.

👉 Sign up to receive 2 video tips by email every week! 👈

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.

data-school

data-science

machine-learning

python

scikit-learn

Significant stargazers

Media

65 followers · starred May 2020

Carlo Lemos

35 followers · starred Oct 2021

Alexander Popkov

32 followers · starred Dec 2022

João G. Atkinson A.

69 followers · starred Jan 2021

Similar repos

justmarkham/scikit-learn-videos

Jupyter notebooks from the scikit-learn video series

3,813

same org

scikit-learn-contrib/sklearn-pandas

Pandas integration with sklearn

2,838

feature-engine/feature_engine

Feature engineering and selection open-source Python library compatible with sklearn.

2,290

ujjwalkarn/DataSciencePython

common data analysis and machine learning tasks using python

5,828

scikit-learn-contrib/category_encoders

A library of sklearn compatible categorical variable encoders

2,510

r0f1/datascience

Curated list of Python resources for data science.

4,680

koaning/scikit-lego

Extra blocks for scikit-learn pipelines.

1,412

jpmml/sklearn2pmml

Python library for converting Scikit-Learn pipelines to PMML

706