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When performing regression or classification which is the correct way to preprocess the data?
15. When performing regression or classification, which of the following is the correct way to preprocess the data? Explanation: You need to always normalize the data first. If not, PCA or other techniques that are used to reduce dimensions will give different results.
How can you prevent a clustering algorithm from getting stuck in bad local?
How can you prevent a clustering algorithm from getting stuck in bad local optima? C.K-Means clustering algorithm has the drawback of converging at local minima which can be prevented by using multiple radom initializations.
What’s the difference between a classification and a regression?
If in the regression problem, input values are dependent or ordered by time then it is known as time series forecasting problem. However, the Classification model will also predict a continuous value that is the probability of happening the event belonging to that respective output class.
When to convert classification to regression in machine learning?
If the class labels in the classification problem do not have a natural ordinal relationship, the conversion from classification to regression may result in surprising or poor performance as the model may learn a false or non-existent mapping from inputs to the continuous output range.
Which is the best definition of a regression problem?
Regression is about finding an optimal function for identifying the data of continuous real values and make predictions of that quantity. Regression with multiple variables as input or features to train the algorithm is known as a multivariate regression problem.
What’s the difference between two class classification problems?
A classification problem requires that examples be classified into one of two or more classes. A classification can have real-valued or discrete input variables. A problem with two classes is often called a two-class or binary classification problem.