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.
Question
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.
Solution
Here is a Python function that does what you asked for. It uses the GridSearchCV function from sklearn.model_selection to search for the optimal parameters for an SVC model. The scoring parameter of GridSearchCV is set to the custom_scoring_function.
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.metrics import make_scorer
def search_svc_hyperparameters(custom_scoring_function):
# Define the parameter grid
param_grid = {'C': [0.1, 1, 10], 'gamma': [0.01, 0.1, 1]}
# Create a SVC model
svc = SVC()
# Create a scorer from the custom scoring function
scorer = make_scorer(custom_scoring_function)
# Create a GridSearchCV object
grid_search = GridSearchCV(svc, param_grid, cv=5, scoring=scorer)
return grid_search
This function returns a GridSearchCV object. To find the optimal parameters and fit the model to your data, you would use the .fit method of the returned object, passing your data as arguments. For example:
grid_search = search_svc_hyperparameters(custom_scoring_function)
grid_search.fit(X, y)
After fitting, you can find the best parameters with grid_search.best_params_ and the best score with grid_search.best_score_. The score will be the highest return value of your custom scoring function, and the parameters that gave this score are the optimal parameters.
Similar Questions
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.
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.
Before running the final line of the code in the snippet below to fit the grid_search object, you are asked to perform the following tasks directly in the code:1. Modify the param_grid to include a new parameter: 'max_features' with values ranging from 1 to 4.2. Fit the grid_search to the training data.3. After fitting, extract and print the best parameter combination and the corresponding cross-validation score.Which of the following options correctly completes these tasks?from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_split, GridSearchCVfrom sklearn.tree import DecisionTreeClassifier# Load the Iris datasetiris = load_iris()X = iris.datay = iris.target# 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)# Setup a basic decision tree classifierdt = DecisionTreeClassifier(random_state=42)# Define a parameter grid over which to optimise the decision treeparam_grid = { 'max_depth': [None, 10, 20, 30], 'min_samples_split': [2, 10, 20]}# Setup the GridSearchCVgrid_search = GridSearchCV(dt, param_grid, cv=5)param_grid.update({'max_features': [1, 2]})grid_search.fit(X_train, y_train)best_params = grid_search.best_params_print(f"Best parameters found: {best_params}, Score: {grid_search.best_score_}")param_grid['max_features'] = range(1, 5)grid_search.fit(X_train, y_train)print(f"Best Params: {grid_search.best_params_}, CV Score: {grid_search.best_score_}")param_grid['max_features'] = [1, 2, 3, 4]grid_search = GridSearchCV(dt, param_grid, cv=5)grid_search.fit(X_train, y_train)print(f"Optimal Parameters: {grid_search.best_params_}, CV Accuracy: {grid_search.best_score_}")param_grid = {'max_features': [1, 2, 3, 4]}grid_search.fit(X_train, y_train)print("Best Parameters:", grid_search.best_params_)print("Best Cross-validation Score:", grid_search.best_score_)
Before running the final line of the code in the snippet below to fit the grid_search object, you are asked to perform the following tasks directly in the code:1. Modify the param_grid to include a new parameter: 'max_features' with values ranging from 1 to 4.2. Fit the grid_search to the training data.3. After fitting, extract and print the best parameter combination and the corresponding cross-validation score.Which of the following options correctly completes these tasks?from sklearn.datasets import load_irisfrom sklearn.model_selection import train_test_split, GridSearchCVfrom sklearn.tree import DecisionTreeClassifier# Load the Iris datasetiris = load_iris()X = iris.datay = iris.target# 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)# Setup a basic decision tree classifierdt = DecisionTreeClassifier(random_state=42)# Define a parameter grid over which to optimise the decision treeparam_grid = { 'max_depth': [None, 10, 20, 30], 'min_samples_split': [2, 10, 20]}# Setup the GridSearchCVgrid_search = GridSearchCV(dt, param_grid, cv=5)
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