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Machine Learning for Beginners in Python: Fast C Hyperparameter Tuning

By on Tuesday, May 25, 2021

Fast C Hyperparameter Tuning

Sometimes the characteristics of a learning algorithm allows us to search for the best hyperparameters significantly faster than either brute force or randomized model search methods.

scikit-learn’s LogisticRegressionCV method includes a parameter Cs. If supplied a list, Cs is the candidate hyperparameter values to select from. If supplied a integer, Cs a list of that many candidate values will is drawn from a logarithmic scale between 0.0001 and and 10000 (a range of reasonable values for C).

Preliminaries


/* Load libraries */
from sklearn import linear_model, datasets

Load Iris Dataset


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

Use Cross-Validation To Find The Best Value Of C


/* Create cross-validated logistic regression */
clf = linear_model.LogisticRegressionCV(Cs=100)

/* Train model */
clf.fit(X, y)
LogisticRegressionCV(Cs=100, class_weight=None, cv=None, dual=False,
           fit_intercept=True, intercept_scaling=1.0, max_iter=100,
           multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,
           refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)

 

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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