SciKit Learn: How to Standardize Your Data
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The document discusses standardizing data as a preprocessing step for machine learning models. It defines standardization as shifting attribute distributions to have a mean of 0 and standard deviation of 1. Standardization is important because some models require normalized data distributions and can behave badly without it. The document provides a Python code recipe using Scikit-Learn to load the Iris dataset, separate features from targets, and standardize the features.
SciKit Learn: How to Standardize Your Data
- 1. How to Standardize Your Data: A ML Recipe
- 2. DAMIAN MINGLE CHIEF DATA SCIENTIST, WPC Healthcare @DamianMingle
- 3. GET THE FULL STORY bit.ly/UseSciKitNow
- 4. What’s Standardization Anyway? • Often referred to as “functions and transformers that change raw feature vectors into a representation that is more suitable for the downstream estimator” • Shifting the distribution of each attribute to have a mean of “0” and a standard deviation of “1”.
- 5. Why Standardization Matters • It’s a common requirement of models • Models may behave badly without it • It’s useful for models that rely on the distribution of attributes such as Gaussian processes.
- 6. Power in SciKit Learn • Preprocessing • Clustering • Regression • Classification • Dimensionality Reduction • Model Selection Power of SciKit Learn
- 7. Let’s Look at ML Recipe Standardization
- 8. The Imports from sklearn.datasets import load_iris from sklearn import preprocessing
- 9. Separate Features from Target iris = load_iris() print(iris.data.shape) X = iris.data y = iris.target
- 10. Standardize the Features normalized_X = preprocessing.scale(X)
- 11. Standardization Recipe # Normalize the data attributes for the Iris dataset. from sklearn.datasets import load_iris from sklearn import preprocessing # load the iris dataset iris = load_iris() print(iris.data.shape) # separate the data from the target attributes X = iris.data y = iris.target # normalize the data attributes normalized_X = preprocessing.scale(X)
- 12. How to Standardize Your Data: An ML Recipe
- 13. DAMIAN MINGLE CHIEF DATA SCIENTIST, WPC Healthcare @DamianMingle
- 14. GET THE FULL STORY bit.ly/UseSciKitNow
- 15. Resources • Society of Data Scientists • SciKit Learn • Also: • Scaling features to a range (MinMaxScaler or MaxAbsScaler) • Scaling sparse data (StandardScaler) • Scaling data with outliers (RobustScaler)