My top 50 scikit-learn tips
If you already know the basics of scikit-learn, but you want to be more efficient and get up-to-date with the latest features, then THIS is the video for you.
My name is Kevin Markham, and I've been teaching Machine Learning in Python with scikit-learn for more than 8 years. Over the next 3 hours, I'm going to share with you my top 50 scikit-learn tips.
Each tip ranges from 2 to 8 minutes, and you can use the timestamp links below to skip along if you're already familiar with a particular tip.
👩💻 Code: https://github.com/justmarkham/scikit-learn-tips
🤖 Learn ML from me: https://courses.dataschool.io/ml-courses
💌 Weekly Data Science tips: https://tuesday.tips/
50 TIPS:
0:00 - Introduction
1:03 - 1. Transform data with ColumnTransformer
4:19 - 2. Seven ways to select columns
8:18 - 3. "fit" vs "transform"
10:53 - 4. Don't use "fit" on new data!
15:05 - 5. Don't use pandas for preprocessing!
19:00 - 6. Encode categorical features
24:07 - 7. Handle new categories in testing data
27:16 - 8. Chain steps with Pipeline
30:19 - 9. Encode "missingness" as a feature
33:12 - 10. Why set a random state?
35:40 - 11. Better ways to impute missing values
41:22 - 12. Pipeline vs make_pipeline
44:08 - 13. Inspect a Pipeline
47:03 - 14. Handle missing values automatically
49:47 - 15. Don't drop the first categorical level
54:15 - 16. Tune a Pipeline
1:01:09 - 17. Randomized search vs grid search
1:05:42 - 18. Examine grid search results
1:08:10 - 19. Logistic regression tuning parameters
1:12:41 - 20. Plot a confusion matrix
1:15:37 - 21. Plot multiple ROC curves
1:17:21 - 22. Use the correct Pipeline methods
1:18:59 - 23. Access model coefficients
1:20:11 - 24. Visualize a decision tree
1:23:57 - 25. Improve a decision tree by pruning it
1:25:23 - 26. Use stratified sampling when splitting data
1:29:40 - 27. Impute missing values for categoricals
1:32:10 - 28. Save a model or Pipeline
1:33:47 - 29. Add multiple text columns to a model
1:35:35 - 30. More ways to inspect a Pip
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Setting up Git and GitHub
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Navigating a GitHub Repository - Part 1
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Forking a GitHub Repository
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Creating a New GitHub Repository
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Copying a GitHub Repository to Your Local Computer
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Committing Changes in Git and Pushing to a GitHub Repository
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Syncing Your GitHub Fork
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Allstate Purchase Prediction Challenge on Kaggle
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Troubleshooting: Updates Rejected When Pushing to GitHub
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Hands-on dplyr tutorial for faster data manipulation in R
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ROC Curves and Area Under the Curve (AUC) Explained
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Going deeper with dplyr: New features in 0.3 and 0.4 (tutorial)
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What is machine learning, and how does it work?
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Setting up Python for machine learning: scikit-learn and Jupyter Notebook
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Getting started in scikit-learn with the famous iris dataset
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Training a machine learning model with scikit-learn
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Comparing machine learning models in scikit-learn
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Data science in Python: pandas, seaborn, scikit-learn
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Selecting the best model in scikit-learn using cross-validation
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How to find the best model parameters in scikit-learn
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How to evaluate a classifier in scikit-learn
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What is pandas? (Introduction to the Q&A series)
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How do I read a tabular data file into pandas?
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How do I select a pandas Series from a DataFrame?
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Why do some pandas commands end with parentheses (and others don't)?
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How do I rename columns in a pandas DataFrame?
Data School
How do I remove columns from a pandas DataFrame?
Data School
How do I sort a pandas DataFrame or a Series?
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How do I filter rows of a pandas DataFrame by column value?
Data School
How do I apply multiple filter criteria to a pandas DataFrame?
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Your pandas questions answered!
Data School
How do I use the "axis" parameter in pandas?
Data School
How do I use string methods in pandas?
Data School
How do I change the data type of a pandas Series?
Data School
When should I use a "groupby" in pandas?
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How do I explore a pandas Series?
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How do I handle missing values in pandas?
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What do I need to know about the pandas index? (Part 1)
Data School
What do I need to know about the pandas index? (Part 2)
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How do I select multiple rows and columns from a pandas DataFrame?
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Machine Learning with Text in scikit-learn (PyCon 2016)
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When should I use the "inplace" parameter in pandas?
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How do I make my pandas DataFrame smaller and faster?
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How do I use pandas with scikit-learn to create Kaggle submissions?
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More of your pandas questions answered!
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How do I create dummy variables in pandas?
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How do I work with dates and times in pandas?
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How do I find and remove duplicate rows in pandas?
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How do I avoid a SettingWithCopyWarning in pandas?
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How do I change display options in pandas?
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How do I create a pandas DataFrame from another object?
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How do I apply a function to a pandas Series or DataFrame?
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Getting started with machine learning in Python (webcast)
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Q&A about Machine Learning with Text (online course)
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Your pandas questions answered! (webcast)
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Machine Learning with Text in scikit-learn (PyData DC 2016)
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Write Pythonic Code for Better Data Science (webcast)
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Web scraping in Python (Part 1): Getting started
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Web scraping in Python (Part 2): Parsing HTML with Beautiful Soup
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Web scraping in Python (Part 3): Building a dataset
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Chapters (31)
Introduction
1:03
1. Transform data with ColumnTransformer
4:19
2. Seven ways to select columns
8:18
3. "fit" vs "transform"
10:53
4. Don't use "fit" on new data!
15:05
5. Don't use pandas for preprocessing!
19:00
6. Encode categorical features
24:07
7. Handle new categories in testing data
27:16
8. Chain steps with Pipeline
30:19
9. Encode "missingness" as a feature
33:12
10. Why set a random state?
35:40
11. Better ways to impute missing values
41:22
12. Pipeline vs make_pipeline
44:08
13. Inspect a Pipeline
47:03
14. Handle missing values automatically
49:47
15. Don't drop the first categorical level
54:15
16. Tune a Pipeline
1:01:09
17. Randomized search vs grid search
1:05:42
18. Examine grid search results
1:08:10
19. Logistic regression tuning parameters
1:12:41
20. Plot a confusion matrix
1:15:37
21. Plot multiple ROC curves
1:17:21
22. Use the correct Pipeline methods
1:18:59
23. Access model coefficients
1:20:11
24. Visualize a decision tree
1:23:57
25. Improve a decision tree by pruning it
1:25:23
26. Use stratified sampling when splitting data
1:29:40
27. Impute missing values for categoricals
1:32:10
28. Save a model or Pipeline
1:33:47
29. Add multiple text columns to a model
1:35:35
30. More ways to inspect a Pip
🎓
Tutor Explanation
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