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Machine Learning for Beginners in Python: How to Find Best Preprocessing Steps During Model Selection

By on Monday, May 24, 2021

Find Best Preprocessing Steps During Model Selection

We have to be careful to properly handle preprocessing when conducting model selection. First, GridSearchCV uses cross-validation to determine which model has the highest performance. However, in cross-validation we are in effect pretending that the fold held out as the test set is not seen, and thus not part of fitting any preprocessing steps (e.g. scaling or standardization). For this reason, we cannot preprocess the data then run GridSearchCV.

Second, some preprocessing methods have their own parameter which often have to be supplied by the user. By including candidate component values in the search space, they are treated like any other hyperparameter be to searched over.

Preliminaries


/* Load libraries */
import numpy as np
from sklearn import datasets
from sklearn.feature_selection import SelectKBest
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline, FeatureUnion
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

/* Set random seed */
np.random.seed(0)

Load Iris Dataset


/* Load data */
iris = datasets.load_iris()
X = iris.data
y = iris.target

Create Proprocessing Object

We are include two different preprocessing steps: principal component analysis and a k-best feature selection.


/* Create a combined preprocessing object */
preprocess = FeatureUnion([('pca', PCA()), ("kbest", SelectKBest(k=1))])

Create Pipeline


/* Create a pipeline */
pipe = Pipeline([('preprocess', preprocess), ('classifier', LogisticRegression())])

Create Search Space Of Hyperparameter Values


/* Create space of candidate values */
search_space = [{'preprocess__pca__n_components': [1, 2, 3],
                 'classifier__penalty': ['l1', 'l2'],
                 'classifier__C': np.logspace(0, 4, 10)}]

/* Create grid search */
clf = GridSearchCV(pipe, search_space, cv=5, verbose=0, n_jobs=-1)

/* Fit grid search */
best_model = clf.fit(X, y)

View Best Model’s Hyperparamters


/* View best hyperparameters */
print('Best Number Of Princpal Components:', best_model.best_estimator_.get_params()['preprocess__pca__n_components'])
print('Best Penalty:', best_model.best_estimator_.get_params()['classifier__penalty'])
print('Best C:', best_model.best_estimator_.get_params()['classifier__C'])

Best Number Of Princpal Components: 3
Best Penalty: l1
Best C: 59.9484250319

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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