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Machine Learning for Beginners in Python: Custom Performance Metric

By on Monday, May 24, 2021

Custom Performance Metric

Preliminaries


/* Load libraries */
from sklearn.metrics import make_scorer, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from sklearn.datasets import make_regression

Create Feature


/* Generate features matrix and target vector */
X, y = make_regression(n_samples = 100,
                          n_features = 3,
                          random_state = 1)

/* Create training set and test set */
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=1)

Train model


/* Create ridge regression object */
classifier = Ridge()

/* Train ridge regression model */
model = classifier.fit(X_train, y_train)

Create Custom Performance Metric

For this example we are just calculating the r-squared score, but we can see that any calculation can be used.


/* Create custom metric */
def custom_metric(y_test, y_pred):
    /* Calculate r-squared score */
    r2 = r2_score(y_test, y_pred)
    /* Return r-squared score */
    return r2

Make Custom Metric A Scorer Object


/* Make scorer and define that higher scores are better */
score = make_scorer(custom_metric, greater_is_better=True)

User Scorer To Evaluate Model Performance


/* Apply custom scorer to ridge regression */
score(model, X_test, y_test)
0.99979061028820582

 

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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