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I know this is an elementary question, but I'm not a python programmer. I have an app that is using the sklearn kit to run regressions on a python server.
Is there a simple command which will return the predictions or the residuals for each and every data record in the sample?
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The residual for each observation is the difference between predicted values of y (dependent variable) and observed values of y . Residual=actual y value−predicted y value,ri=yi−^yi. Residual = actual y value − predicted y value , r i = y i − y i ^ .
LinearRegression fits a linear model with coefficients w = (w1, …, wp) to minimize the residual sum of squares between the observed targets in the dataset, and the targets predicted by the linear approximation.
linear_model is a class of the sklearn module if contain different functions for performing machine learning with linear models. The term linear model implies that the model is specified as a linear combination of features.
In sklearn to get predictions use .predict(x)
modelname.fit(xtrain, ytrain)
prediction = modelname.predict(x_test)
residual = (y_test - prediction)
If you are using an OLS stats model
OLS_model = sm.OLS(y,x).fit() # training the model
predicted_values = OLS_model.predict() # predicted values
residual_values = OLS_model.resid # residual values
One option is to use fit() to get predictions and residual is simply the difference between the actual value and predictions.
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