I wanted to know how a random forest is actually made, let us say i have some small three feature (continuous values/ numerical values) and a target variable (continuous) data set and wanted to make a random forest that has four sub trees. How could i do this and how is my model going to learn from this? I wanted to get the basic idea so i could explain others with a pen and pencil showing a small demo with a sample data by constructing a small random forest and how does the model learn from the forest or how it predicts the output? ]]>

https://onclick360.com/cost-function-in-machine-learning/ ]]>

For a linear regression, there is a formula to explain the contribution of each independent variable

For a decision tree. there is a map to show the segmentation by each independent variable.

For a random forest, is there any clear ,easy and direct explanation about the fit result？ ]]>

What is the meaning behind the division with two?

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