New tips are posted on LinkedIn, Twitter, and Facebook every weekday!
👉 Sign up to receive 5 tips by email every week 👈
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 |
|
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.
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.
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Due to changes in the scikit-learn API, a small percentage of the code shown in the videos is out-of-date.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.
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.
Yes! In 2019, I posted 100 pandas tricks. I also created a video featuring my top 25 pandas tricks.
© 2020 Data School. All rights reserved.