K-fold cross-validation Interview Questions

What is K-fold cross-validation?

K-fold cross-validation is a method of assessing the accuracy of a machine learning model. It involves partitioning the data into k subsets, training the model on k-1 subsets, and testing it on the remaining subset. This is repeated k times, with each subset serving as the test set once. The average accuracy across all k iterations is then reported.

How to perform K-fold cross-validation step by step approach in detail?

1. Randomly split your dataset into k equal partitions.
2. For each k-fold in your dataset perform the following:
   - Retain k-1 partitions as the training set.
   - Use the remaining 1 partition as the test set.
   - Train your model on the training set.
   - Evaluate it on the test set and record the scores.
3. Aggregate the model scores and estimate the generalization performance of your model.

How to evaluate the result of K-fold cross-validation?

The following are the various ways to evaluate the result of K-fold cross-validation.

• Overall accuracy of the model. This can be done by taking the mean of the accuracy scores for each fold.
• Precision and Recall for each fold. This can be done by taking the mean of the precision and recall scores for each fold.
• f1 score for each fold. This can be done by taking the mean of the f1 scores for each fold.

In generals terms, you can further check the following evaluate the results of K-fold cross-validation apart from the aforementioned ones.

• Calculate the mean and standard deviation of the accuracy scores for each fold. This will give you an idea of how accurate the model is, and how stable the results are.
• Compare the results of different runs of cross-validation. This will help you see if the results are sensitive to the particular folds that are used.
• Observe the confusion matrix for each fold to see where the misclassifications are happening. This can help you understand why the model is making certain mistakes, and give you ideas for how to improve it.

How to calculate Precision and Recall of K-fold cross-validation?

Precision and recall can be calculated for each fold of a k-fold cross-validation using the following formulae:

Precision = TP / (TP + FP)

Recall = TP / (TP + FN)

Where,

• TP is the number of true positives,
• FP is the number of false positives, and
• FN is the number of false negatives.

Accuracy metrics in Model Validation: Precision, Recall, F1 Score, True Positive, False Positive, False Negative, Interview questions

Most frequently asked data science interview questions for Accuracy metrics: Precision, Recall, TP, FP, FN

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Write a simple program in Python to perform K-fold cross-validation that also does calculates model accuracy?

from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# creating a KFold object with 5 splits 
folds = KFold(n_splits = 5, shuffle = True, random_state = 101)

# specify range of hyperparameters
# Set the parameters by cross-validation
hyper_params = [ {'n_features': 2, 'coef0': 1, 'degree': 3, 'gamma': 1},
                {'n_features': 2, 'coef0': 1, 'degree': 4, 'gamma': 1},
                {'n_features': 2, 'coef0': 1, 'degree': 5, 'gamma': 1},
                {'n_features': 2, 'coef0': 1, 'degree': 6, 'gamma': 1},
                {'n_features': 2, 'coef0': 1, 'degree': 7, 'gamma': 1}]

# empty list to store results
cv_results = []

# fill the empty list with the results of the grid search
for hyperparam in hyper_params:
    clf = SVC(**hyperparam)

    # cross validation 
    scores = cross_val_score(clf, X_train, y_train, cv=folds, scoring='accuracy') 

    # append mean of scores for each hyperparameter to cv_results
    cv_results.append(scores.mean())


# changing to misclassification error
MSE = [1 - x for x in cv_results]

# determining best hyperparameters from results
optimal_hyperparams = hyper_params[MSE.index(min(MSE))]
print("Best hyperparameters:", optimal_hyperparams)


# model with optimal hyperparameters
model = SVC(**optimal_hyperparams)
model.fit(X_train, y_train)


# predictions using model with optimal hyperparameters
predictions = model.predict(X_test)


# accuracy score on test data using model with optimal hyperparameters
accuracy = accuracy_score(y_test, predictions) * 100
print("Accuracy on test set: %.4f%%" % accuracy)

What are the advantages and disadvantages of K-fold cross-validation?

Advantages of K-fold cross-validation

• K-fold cross-validation is a more robust method for estimating the performance of a machine learning algorithm because it reduces the chance of overfitting on the training set.
• K-fold cross-validation can be used to compare the performance of different machine learning algorithms.
• K-fold cross-validation can be used to tune the hyperparameters of a machine learning algorithm.

Disadvantages of K-fold cross-validation

• K-fold cross-validation is more computationally expensive than other methods for estimating the performance of a machine learning algorithm.