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- Compare the effect of different scalers on data with outliers
- sklearn.preprocessing.MaxAbsScaler (Python class, in MaxAbsScaler)
- sklearn.preprocessing.MinMaxScaler (Python class, in MinMaxScaler)
- sklearn.preprocessing.RobustScaler (Python class, in RobustScaler)
- sklearn.preprocessing.StandardScaler (Python class, in StandardScaler)
- MaxAbsScaler
- MinMaxScaler
- RobustScaler
- Compare the effect of different scalers on data with outliers > MaxAbsScaler
- Compare the effect of different scalers on data with outliers > MinMaxScaler
- Compare the effect of different scalers on data with outliers > RobustScaler
- 1.17. Neural network models (supervised)
- 1.5. Stochastic Gradient Descent
- 10. Common pitfalls and recommended practices
- 3.1. Cross-validation: evaluating estimator performance
- 6.1. Pipelines and composite estimators
- 6.3. Preprocessing data
- Column Transformer with Mixed Types
- Comparing Nearest Neighbors with and without Neighborhood Components Analysis
- Computation times
- Crafting a minimal reproducer for scikit-learn
- Displaying Pipelines
- Evaluation of outlier detection estimators
- Examples
- Explicit feature map approximation for RBF kernels
- Faces recognition example using eigenfaces and SVMs
- fetch_california_housing
- Importance of Feature Scaling
- Introducing the
set_outputAPI - LogisticRegression
- LogisticRegressionCV
- Map data to a normal distribution
- maxabs_scale
- minmax_scale
- MNIST classification using multinomial logistic + L1
- Nearest Neighbors Classification
- normalize
- Normalizer
- Older Versions
- Pipeline
- Pipelining: chaining a PCA and a logistic regression
- Post-hoc tuning the cut-off point of decision function
- power_transform
- PowerTransformer
- Preprocessing
- quantile_transform
- QuantileTransformer
- RBF SVM parameters
- Recursive feature elimination
- Release Highlights for scikit-learn 1.2
- Ridge
- ridge_regression
- RidgeClassifier