We can evaluate the performance of an algorithm of a classification problem using the logistic loss function. The logistic loss of a classification algorithm is given by the following formula:
We can use the following Python code to calculate log loss for a classification problem.
from sklearn.model_selection import KFold from sklearn.linear_model import LogisticRegression from sklearn.model_selection import cross_val_score import pandas data = pandas.read_csv("diabetes.csv") D = data.values X = D[:, :-1] y = D[:, -1] k_fold = KFold(n_splits=10, shuffle=True, random_state=1) classifier = LogisticRegression(solver="liblinear") results = cross_val_score(classifier, X, y, cv=k_fold, scoring="neg_log_loss") mean_score = results.mean() print("Negative Log Loss: ", mean_score)
Here, we are first using the pandas Python library to read the Pima Indians Diabetes dataset. The dataset contains various …






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