Contents
How do we predict one variable from another?
Linear regression is used to predict the value of one variable from another variable. Since it is based on correlation, it cannot provide causation.
Which correlation is the weakest?
The weakest linear relationship is indicated by a correlation coefficient equal to 0. A positive correlation means that if one variable gets bigger, the other variable tends to get bigger. A negative correlation means that if one variable gets bigger, the other variable tends to get smaller.
How to find which variables matter most for prediction of another variable?
Here is a non exhaustive list of possibilities : With the RandomForestClassifier (or RandomForestRegressor depending on y) of sklearn.ensemble, you can use feature_importances_ method to get which one is used the most at tree nodes. Note : it works with any decision tree estimator.
How to predict a variable based on occupation?
The first variable occupation has only 8 values that it can take, and class can only take 3. I am trying to predict the class variable based on the occupation. I have an idea about predicting continuous independent variables with continuous dependent variables (for example, linear regression).
How to predict a categorical variable with regression?
Especially since there is no specific scaling order to the variable. Perhaps turning occupation into a new binary variable “professional” “non-professional”. But I still wouldn’t know how to compare the new binary output to the class variable. The easiest way is to break your data down into eight groups.
Which is variable X explains y the best?
As to assert which variable x i explains y the best, one might use different ways. Just to mention that the feature importance is not absolute ; it relies on the technique (or estimator) you use to address this question. Here is a non exhaustive list of possibilities :