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تحميل scikit learn

Programming 2025-05-19

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عدد الدروس 51

قسم Programming

Use ColumnTransformer to apply different preprocessing to different columns

Seven ways to select columns using ColumnTransformer

What is the difference between fit and transform

Use fit transform on training data but transform only on testing new data

Four reasons to use scikit learn not pandas for ML preprocessing

Encode categorical features using OneHotEncoder or OrdinalEncoder

Handle unknown categories with OneHotEncoder by encoding them as zeros

Use Pipeline to chain together multiple steps

Add a missing indicator to encode missingness as a feature

Set a random state to make your code reproducible

Impute missing values using KNNImputer or IterativeImputer

What is the difference between Pipeline and make pipeline

Examine the intermediate steps in a Pipeline

HistGradientBoostingClassifier natively supports missing values

Three reasons not to use drop first with OneHotEncoder

Use cross val score and GridSearchCV on a Pipeline

Try RandomizedSearchCV if GridSearchCV is taking too long

Display GridSearchCV or RandomizedSearchCV results in a DataFrame

Important tuning parameters for LogisticRegression

Plot a confusion matrix

Compare multiple ROC curves in a single plot

Use the correct methods for each type of Pipeline

Display the intercept and coefficients for a linear model

Visualize a decision tree two different ways

Prune a decision tree to avoid overfitting

Use stratified sampling with train test split

Two ways to impute missing values for a categorical feature

Save a model or Pipeline using joblib

Vectorize two text columns in a ColumnTransformer

Four ways to examine the steps of a Pipeline

Shuffle your dataset when using cross val score

Use AUC to evaluate multiclass problems

Use FunctionTransformer to convert functions into transformers

Add feature selection to a Pipeline

Don t use values when passing a pandas object to scikit learn

Most parameters should be passed as keyword arguments

Create an interactive diagram of a Pipeline in Jupyter

Get the feature names output by a ColumnTransformer

Load a toy dataset into a DataFrame

Estimators only print parameters that have been changed

Drop the first category from binary features only with OneHotEncoder

Passthrough some columns and drop others in a ColumnTransformer

Use OrdinalEncoder instead of OneHotEncoder with tree based models

Speed up GridSearchCV using parallel processing

Create feature interactions using PolynomialFeatures

Ensemble multiple models using VotingClassifer or VotingRegressor

Tune the parameters of a VotingClassifer or VotingRegressor

Access part of a Pipeline using slicing

Tune multiple models simultaneously with GridSearchCV

Adapt this pattern to solve many Machine Learning problems

My top 50 scikit learn tips

Programming
2025-05-19