Rapid Fire on Machine Learning

A voting model is an ensemble model which combines several classifiers but to produce the final result, in case of a classification-based model, takes into account, the classification of a certain data point of all the models and picks the most vouched/voted/generated option from all the given classes in the target column.   In machine … Read more

Cross-validation is a technique which is used to increase the performance of a machine learning algorithm, where the machine is fed sampled data out of the same data for a few times. The sampling is done so that the dataset is broken into small parts of the equal number of rows, and a random part … Read more

Class imbalance can be dealt with in the following ways: Using class weights Using Sampling Using SMOTE Choosing loss functions like Focal Loss   Dealing with class imbalance in a classification problem is crucial to ensure that the machine learning model does not favor the majority class and can effectively learn patterns from minority classes. … Read more

No, ARIMA model is not suitable for every type of time series problem. There are situations where ARMA model and others also come in handy. ARIMA is best when different standard temporal structures require to be captured for time series data.   No, the ARIMA (AutoRegressive Integrated Moving Average) model may not be a good … Read more

It is a situation in which the variance of a variable is unequal across the range of values of the predictor variable. It should be avoided in regression as it introduces unnecessary variance.   Heteroscedasticity is a term used in statistics and econometrics to describe a situation where the variability of the errors (residuals) in … Read more