Trying to do SVR for multiple outputs. Started by hyper-parameter tuning which worked for me. Now I want to create the model using the optimum parameters but I am getting an error. How to fix this?
from sklearn.svm import SVR from sklearn.model_selection import GridSearchCV from sklearn.multioutput import MultiOutputRegressor svr = SVR() svr_regr = MultiOutputRegressor(svr) from sklearn.model_selection import KFold kfold_splitter = KFold(n_splits=6, random_state = 0,shuffle=True) svr_gs = GridSearchCV(svr_regr, param_grid = {'estimator__kernel': ('linear','poly','rbf','sigmoid'), 'estimator__C': [1,1.5,2,2.5,3,3.5,4,4.5,5,5.5,6,6.5,7,7.5,8,8.5,9,9.5,10], 'estimator__degree': [3,8], 'estimator__coef0': [0.01,0.1,0.5], 'estimator__gamma': ('auto','scale'), 'estimator__tol': [1e-3, 1e-4, 1e-5, 1e-6]}, cv=kfold_splitter, n_jobs=-1, scoring='r2') svr_gs.fit(X_train, y_train) print(svr_gs.best_params_) #print(gs.best_score_)
Output:
{'estimator__C': 10, 'estimator__coef0': 0.01, 'estimator__degree': 3, 'estimator__gamma': 'auto', 'estimator__kernel': 'rbf', 'estimator__tol': 1e-06}
Trying to create a model using the output:
SVR_model = svr_regr (kernel='rbf',C=10, coef0=0.01,degree=3, gamma='auto',tol=1e-6,random_state=42) SVR_model.fit(X_train, y_train) SVR_model_y_predict = SVR_model.predict((X_test)) SVR_model_y_predict
Error:
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) /var/folders/mm/r4gnnwl948zclfyx12w803040000gn/T/ipykernel_96269/769104914.py in <module> ----> 1 SVR_model = svr_regr (estimator__kernel='rbf',estimator__C=10, 2 estimator__coef0=0.01,estimator__degree=3, 3 estimator__gamma='auto',estimator__tol=1e-6,random_state=42) 4 5 TypeError: 'MultiOutputRegressor' object is not callable
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Answer
Please consult the MultiOutputRegressor docs.
The regressor you got back is the model.
It is not a method, but it does offer
a bunch of fun methods that you can call,
such as .fit()
, .predict()
, and .score()
.
You are trying to specify kernel
and a few
other parameters.
It appears you wanted to offer those
to SVR()
, at the top of your code.