3 Reasons to Use a Random Forest Over a Neural Network

Neural networks have been shown to outperform a number of machine learning algorithms in many industry domains. They keep learning until it comes out with the best set of features to obtain a satisfying predictive performance. However, a neural network will scale your variables into a series of numbers that once the neural network finishes the learning stage, the features become indistinguishable to us.

If all we cared about was the prediction, a neural net would be the de-facto algorithm used all the time. But in an industry setting, we need a model that can give meaning to a feature/variable to stakeholders. And these stakeholders will likely be anyone other than someone with a knowledge of deep learning or machine learning.

A Comprehensive Guide to Random Forest in R

Random Forest in R

Random Forest in R

With the demand for more complex computations, we cannot rely on simplistic algorithms. Instead, we must utilize algorithms with higher computational capabilities and one such algorithm is the random forest. In this blog post on random forest In R, you’ll learn the fundamentals of random forest along with its implementation by using the R Language.

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What Is Classification?

Classification is the method of predicting the class of a given input data point. Classification problems are common in machine learning and they fall under the Supervised learning method.