![]() ![]() ![]() Unlike the classification problem, here the focus is on the relationship between a dependent variable and one or more independent variables (usually more than one). The regression analysis is a statistical/machine learning process for estimating the relationships by utilizing widely used techniques such as modeling and analyzing several variables. However, we will see how to use the RF algorithm to predict the song's year by converting the classification problem into an equivalent regression problem. The objective here is to predict the release year of the songs from the available audio features, which is typically a classification problem. The first part of this article covered how to use the RF algorithm as a classifier for predicting appropriate classes of the upcoming features (i.e., test set) by learning from the available features (i.e., training set). However, in this article, we will see how to use the same algorithm as a regressor with Spark 2.0 on the YearPredictionMSD (Year Prediction Million Song Database) dataset. ![]() Typically, the Random Forest (RF) algorithm is used for solving classification problems and making predictive analytics (i.e., in supervised machine learning technique). ![]()
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