What is logistic regression and what are its pros and cons?
Let’s discuss some advantages and disadvantages of Linear Regression. Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting.
Which of the following metrics can be used to compare different logistic regression models?
These (R Squared, Adjusted R Squared, F Statistics , RMSE / MSE / MAE ) are some metrics which you can use to evaluate your regression model.
What are the evaluation metrics for linear regression?
Evaluation metrics for a linear regression model Evaluation metrics are a measure of how good a model performs and how well it approximates the relationship. Let us look at MSE, MAE, R-squared, Adjusted R-squared, and RMSE. Mean Squared Error (MSE)
Which is the right metric to use for multi-classification?
Yes, the difference in F1 score reflects the model performance. ROC-AUC gives a decent score to model 1 as well which is nota good indicator of its performance. Hence we should be careful while picking roc-auc for imbalanced datasets. Which metric should you use for multi-classification? We have further three types of non-binary classification:
When to revisit metrics in your business plan?
Some employers revisit metrics on an annual basis as part of the annual business planning cycle, while others do so at some other interval. However, any major shift in strategy, organizational structure or markets may require an off-cycle look at metrics.
Why is it important to understand the context before choosing a metric?
Similarly each machine learning model is trying to solve a problem with a different objective using a different dataset and hence, it is important to understand the context before choosing a metric. In the first blog, we will cover metrics in regression only. Most of the blogs have focussed on classification metrics like precision, recall, AUC etc.