You train a Support Vector Machine with a RBF kernel and obtain an accuracy of 100% on the training data and 50% on the validation data. What should you do to the parameter Gamma?1 pointIncrease GammaDecrease GammaLeave Gamma unchanged

Question 1You train a Support Vector Machine and obtain an accuracy of 100% on the training data and 50% on the validation data. This is an example of:1 pointOverfitting Underfitting A good model

Write a function that should:Instantiate a SVC model.Train the SVC model with default parameters.Return the trained SVC model.Function specifications:Should take two numpy arrays as input in the form (X_train, y_train).Should return an sklearn SVC model which has a random state of 40 and gamma set to 'auto'.The returned model should be fitted to the data.

The next step is define a set of SVC hyperparameters to search over. Write a function that searches for optimal parameters using the given dictionary of hyperparameters:C_list = [0.1, 1, 10]{C: 0.1, 1, 10}gamma_list = [0.01, 0.1, 1]{gamma: 0.01, 0.1, 1}D = {'C':[0.1, 1, 10], 'gamma': [0.01, 0.1, 1]}and using custom_scoring_function from Question 3 above as a custom scoring function (Hint: Have a look at at the make_scorer object in sklearn metrics).Function specifications:Should define a parameter grid using the given list of SVC hyperparametersShould return an sklearn GridSearchCV object with a cross validation of 5.Should return a value rounded to 4 decimal places.

Which kernel function is commonly used in a Support Vector Machine Classifier when dealing with non-linearly separable data?Review LaterLinear KernelPolynomial KernelRadial Basis Function (RBF) KernelSigmoid Kernel

Which kernel can be used in scikit-learn's SVM implementation?Answer areaLinearPolynomialRadial Basis Function (RBF)All of the above