Difference between Decision tree and Random Forest?
Decision tress – A decision tree is a machine learning model that is used for classification and regression. It is constructed in a way that splits the given data into smaller subsets, each of which contains data points with the same properties. The decision tree algorithm then recursively splits these subsets until it reaches a leaf node, which contains all the data points that are classified as positive or negative. The decision tree can be constructed in a variety of ways, but typically consists of the following: Based on this decision tree, the probability that individual “x” would be classified as positive or negative is calculated. The probabilities for each level are processed to determine if the classification is “positive” or “negative.”
Random Forest – Random Forest is an ensemble machine learning algorithm that makes predictions by combining many decision trees into one model. Random forest models are often more accurate than single-tree models because they’re less prone to overfitting and they’re more robust against new types of data.
Why you might use this:
When learning patterns or associations between variables, random forest models are a good choice to make sure that your model isn’t overfitting.
The algorithm of:
Random Forest models can be used for classification or regression problems. They allow you to make predictions by combining decision trees together, hence there are many possible hypotheses that could be chosen. The ‘Random Forest’ algorithm builds a number of decision trees, each with its own split point and weighting. The model is then trained to make predictions based on the values of the features selected in one tree.