Which of the following method gives the best fit for the Logistic Regression model?

Which of the following method gives the best fit for the Logistic Regression model?

Just as ordinary least square regression is the method used to estimate coefficients for the best fit line in linear regression, logistic regression uses maximum likelihood estimation (MLE) to obtain the model coefficients that relate predictors to the target.

Which of the following evaluation metric can be used to assess a Logistic Regression model?

7) One of the very good methods to analyze the performance of Logistic Regression is AIC, which is similar to R-Squared in Linear Regression.

Why Logistic Regression is better than naive Bayes?

If the data set follows the bias then Naive Bayes will be a better classifier. Both Naive Bayes and Logistic regression are linear classifiers, Logistic Regression makes a prediction for the probability using a direct functional form where as Naive Bayes figures out how the data was generated given the results.

Which metric will you use for judging a Logistic Regression model?

Root Mean Squared Error (RMSE) RMSE is the most popular evaluation metric used in regression problems.

What types of problems are best suited for logistic regression?

Logistic regression is a powerful machine learning algorithm that utilizes a sigmoid function and works best on binary classification problems, although it can be used on multi-class classification problems through the “one vs. all” method. Logistic regression (despite its name) is not fit for regression tasks.

What is the measure of goodness of fit for the estimated regression equation?

R squared, the proportion of variation in the outcome Y, explained by the covariates X, is commonly described as a measure of goodness of fit.

What is a reasonable evaluation metric for measuring the prediction performance of a classification model on data that has class imbalance?

The most common metric used to evaluate the performance of a classification predictive model is classification accuracy. Typically, the accuracy of a predictive model is good (above 90\% accuracy), therefore it is also very common to summarize the performance of a model in terms of the error rate of the model.

How is logistic regression different from naive Bayes?

Naïve Bayes is a classification method based on Bayes’ theorem that derives the probability of the given feature vector being associated with a label. Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class.

Is logistic regression faster than naive Bayes?

I can’t find a reference now, but e.g. in classification, naive Bayes converges quicker but has typically a higher error than logistic regression. On small datasets you’d might want to try out naive Bayes, but as your training set size grows, you likely get better results with logistic regression.

What is good accuracy in logistic regression?

Sklearn has a cross_val_score object that allows us to see how well our model generalizes. So the range of our accuracy is between 0.62 to 0.75 but generally 0.7 on average.