Rapid Fire on Machine Learning

When the model’s predicted value is very close to the actual value the condition is known as low bias. In this condition, we can use bagging algorithms like random forest regressor. When your model is suffering from low bias and high variance, it indicates that the model is fitting the training data very well but … Read more

it is the process of reducing the size of the feature matrix. We try to reduce the number of columns so that we get a better feature set either by combining columns or by removing extra variables. Dimension reduction in machine learning refers to the process of reducing the number of input variables or features … Read more

In ensemble learning, many base models like classifiers and regressors are generated and combined together so that they give better results. It is used when we build component classifiers that are accurate and independent. There are sequential as well as parallel ensemble methods. Ensemble learning is a powerful technique in machine learning where multiple models … Read more

A Naive Bayes classifier converges very quickly as compared to other models like logistic regression. As a result, we need less training data in case of naive Bayes classifier. The main advantages of Naive Bayes classifier are: Simplicity and Ease of Implementation: Naive Bayes classifiers are simple and easy to implement. They are straightforward probabilistic … Read more

We split the given data set into two different sections namely,’Training set’ and ‘Test Set’. ‘Training set’ is the portion of the dataset used to train the model. ‘Testing set’ is the portion of the dataset used to test the trained model. In the context of machine learning, both the training set and the test … Read more