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.
Question
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.
Solution
Here is a Python function that fits your specifications:
from sklearn.svm import SVC
import numpy as np
def train_svc_model(X_train: np.ndarray, y_train: np.ndarray) -> SVC:
# Instantiate a SVC model with random state of 40 and gamma set to 'auto'
model = SVC(random_state=40, gamma='auto')
# Train the SVC model with default parameters
model.fit(X_train, y_train)
# Return the trained SVC model
return model
This function takes two numpy arrays as input, X_train and y_train, which represent the training data and the corresponding labels, respectively. It then creates an instance of the SVC class from the sklearn.svm module, with a random state of 40 and gamma set to 'auto'. The model is then trained using the fit method, and the trained model is returned.
Similar Questions
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 code snippet can be used to fill in the missing lines of code to train the SVM classifier, predict the test set results, and print the classification report?from sklearn.datasets import make_classificationfrom sklearn.model_selection import train_test_splitfrom sklearn.svm import SVCfrom sklearn.metrics import classification_report# Generate a synthetic datasetX, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)# Split the data into training and test setsX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)# Initialise the SVM classifier with a radial basis function kernelsvm_rbf = SVC(kernel='rbf')# Fit the classifier to the training data# [Your Code Here] - Line to add for fitting the model# Predict the test set results# [Your Code Here] - Line to add for making predictions# Generate and print the classification report# [Your Code Here] - Line to add for printing the classification report
What is the best value of the parameter 'C' for the SVC according to the grid search? Modify the code to print the best parameter.from sklearn.datasets import load_digitsfrom sklearn.model_selection import GridSearchCVfrom sklearn.svm import SVCimport numpy as np# Load a datasetdigits = load_digits()X = digits.datay = digits.target# Initialise an SVC (Support Vector Classifier) with a linear kernelsvm = SVC(kernel='linear')# Define parameter range for C (regularisation parameter)param_grid = {'C': np.logspace(-3, 3, 7)}# Setup the grid search with cross-validationgrid_search = GridSearchCV(svm, param_grid, cv=5, scoring='accuracy')# Fit grid searchgrid_search.fit(X, y)# insert code here
Write a function that returns the best hyperperameters for a given model (i.e. the GridSearchCV).Function specifications:Should take in an sklearn GridSearchCV object.Should return a dictionary of optimal parameters for the given model.
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
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