for linear regression type of problem, you can simply create the Output layer without any activation function as we are interested in numerical values without any transformation. more info : https://machinelearningmastery.com/regression-tutorial-keras-deep-learning-library-python/ for classification : You can use sigmoid, tanh, Softmax etc.
Most of the answers I found (including here) emphasize on the difference between R2 and Explained Variance Score, that is: The Mean Residue (i.e. The Mean of Error). However, there is an important question left behind, that is: Why on earth I need to consider The Mean of Error? Refresher: R2: is the Coefficient of … Read more
Short answer: Transpose your pred vector using tf.transpose(pred). Longer answer: The problem is that pred (the predictions) and y (the labels) are not of the same shape: one is a row vector and the other a column vector. Apparently when you apply an element-wise operation on them, you’ll get a matrix, which is not what … Read more
LOESS doesn’t have an explicit concept for standard error. It just doesn’t mean anything in this context. Since that’s out, your stuck with the brute-force approach. Bootstrap your data. Your going to fit a LOESS curve to the bootstrapped data. See the middle of this page to find a pretty picture of what your doing. … Read more
You may try mlxtend which got various selection methods. from mlxtend.feature_selection import SequentialFeatureSelector as sfs clf = LinearRegression() # Build step forward feature selection sfs1 = sfs(clf,k_features = 10,forward=True,floating=False, scoring=’r2′,cv=5) # Perform SFFS sfs1 = sfs1.fit(X_train, y_train)
For generating polynomial features, I assume you are using sklearn.preprocessing.PolynomialFeatures There’s an argument in the method for considering only the interactions. So, you can write something like: poly = PolynomialFeatures(interaction_only=True,include_bias = False) poly.fit_transform(X) Now only your interaction terms are considered and higher degrees are omitted. Your new feature space becomes [x1,x2,x3,x1*x2,x1*x3,x2*x3] You can fit your … Read more