k nearest neighbors with well defined k value
so let us understand how we can chooose the perfect k for our model from the last model i had prepared a function def regression(model): x_train,x_test,y_train,y_test= train_test_split(x,y,test_size=0.2) reg_all=model reg_all.fit(x_train,y_train) y_predict=reg_all.predict(x_test) rmse_value=np.sqrt(mean_squared_error(y_test,y_predict)) print("rms error={}".format(rmse_value)) i have prepared cross value squared to get the mean of rmse where k=3 denote the mean of three iterated value of rmse Lasso is a way to conterect over fitting (we can also use ridge) to check. from sklearn.model_selection import cross_val_score from sklearn.linear_model import Lasso def regression_cv(model,k=3): scores=cross_val_score(model,x,y,scoring='neg_mean_squared_error',cv=k) rmse=np.sqrt(-scores) print('reg rmse:',rmse) print('reg mean:',rmse.mean()) importi...
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