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De facto many classificators like logistic regression, random forest, decision trees and SVM all work fine with both types of data. I suspect it would be hard to find an algorithm which works with continous data but cannot handle categorical data at all.

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Jul 12, 2020 · Random Forest Accuracy: 0.978. Confusion Matrix for our Machine Learning Models. Now I will construct a confusion matrix to visualize predictions made by our classifier and evaluate the accuracy of our machine learning classification. Random Forest

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I am the Director of Machine Learning at the Wikimedia Foundation.I have spent over a decade applying statistical learning, artificial intelligence, and software engineering to political, social, and humanitarian efforts.

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However, you will frequently have data that contains categorical variables and not continuous variables. For instance, a dataset could contain occurrences of some event in specific countries. The countries are categorical variables. In order to properly use linear regression, these categorical variables must be converted into continuous variables.

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A combination of first Random Forest than Neural Network had the accuracy of 72.0% while the opposite one turned out 73.5%. Comparing to the linear function and Neural Network, Random Forest exhibited superior advantages of more than 5% in its results. We can also see improvements in our “Neural Network + Random Forest” combination.

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Nov 15, 2018 · Based on this, essentially what an isolation forest does, is construct a decision tree for each data point. In each tree, each split is based on selecting a random variable, and a random value on that variable. Subsequently, data points are ranked on how little splits it took to identify them.

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The More Trees, the Better! Random Forest improves the accuracy of the model without over fitting the data and overcomes the limitations of Decision Trees. RF also handles unbalanced data with great efficiency. Random forest is one of the sophisticated algorithms used to solve regression and classification problem.