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π€ scikit-learn tip β‘
Q: What is the difference between the "fit" and "transform" methods?
"fit": transformer learns something about the data
"transform": it uses what it learned to do the data transformation
See examples π
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π€ Scikit-learn Tip π‘:
To apply different preprocessing to distinct columns, use ColumnTransformer:
- select columns from a DataFrame by name
- passthrough or drop columns that aren't specified
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π€β‘ NEW VIDEO:
Use the correct methods for each type of Pipeline
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π€β‘ NEW VIDEO:
Compare multiple ROC curves in a single plot!
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π€β‘ NEW VIDEO:
Plot a confusion matrix in one line of code!
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π€β‘ NEW VIDEO:
Important tuning parameters for LogisticRegression
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π€β‘ NEW VIDEO:
Display GridSearchCV results in a pandas DataFrame
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π€β‘ NEW VIDEO:
Have you been tuning your models/pipelines with *grid search*?
Find out why *randomized search* might be a better fit for your needs!
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π€β‘ NEW VIDEO:
Use cross_val_score and GridSearchCV on a Pipeline
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π€β‘ scikit-learn tip #15:
In most cases, I recommend that you *don't* use drop='first' with OneHotEncoder (which drops the first level after dummy encoding).
Here's why: https://t.co/zeuM1eT2Iv
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π€β‘ NEW VIDEO:
Examine the intermediate steps in a Pipeline
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π€β‘ NEW VIDEO:
What is the difference between Pipeline and make_pipeline?
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π€β‘ NEW VIDEO:
Set a "random_state" to make your code reproducible
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π€β‘ NEW VIDEO:
Add a missing indicator to encode "missingness" as a feature
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π€β‘ NEW VIDEO:
Use Pipeline to chain together multiple steps
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π€β‘ NEW VIDEO:
Handle unknown categories with OneHotEncoder by encoding them as zeros
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π€β‘ NEW VIDEO:
Encode categorical features using OneHotEncoder or OrdinalEncoder
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π€β‘ NEW VIDEO:
Four reasons to use scikit-learn (not pandas) for ML preprocessing
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π€β‘ NEW VIDEO:
Use "fit_transform" on training data, but "transform" (only) on testing/new data
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π€β‘ NEW VIDEO:
What is the difference between "fit" and "transform"?
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