# SVM Implementation Steps: 8 and 9

This lesson will go over steps 8-9 of implementing support vector machines.

## We'll cover the following

## 8) Grid search

You can improve the accuracy of our model using a technique called grid search to help us find the optimal hyperparameters for this algorithm. While many hyperparameters belong to

The hyperparameter C controls the cost of misclassification on the training data. In other words, C regulates the extent to which misclassified cases (placed on the wrong side of the margin) are ignored.

This flexibility in the model is referred to as a “soft margin,” and ignoring cases that cross over the soft margin can lead to a better fit. In practice, the lower C is, the more errors the soft margin is permitted to ignore. A C value of ‘0’ enforces no penalty on misclassified cases.

Gamma refers to the Gaussian radial basis function and the influence of the support vector. In general, a small gamma produces models with high bias and low variance. Conversely, a large gamma leads to low bias and high variance in the model.

Grid search allows us to list a range of values to test for each hyperparameter. An automated voting process then takes place to determine the optimal value for each hyperparameter.

Note:Grid search must examine each combination of hyperparameters. It can take a long time to run, particularly as you add more values for testing. In this exercise, you will test three values for each hyperparameter.

You should use the following code in a new cell within the same notebook.

Begin by stating the hyperparameters you wish to test.

```
hyperparameters = {'C':[10,25,50],'gamma':[0.001,0.0001,0.00001]}
```

Link your specified hyperparameters to `GridSearchCV`

and the `SVC`

algorithm under a new variable name.

```
grid = GridSearchCV(SVC(),hyperparameters)
```

Next, fit grid search to the X and y training data.

```
grid.fit(X_train, y_train)
```

You can now use the `grid.best_params_`

function to review the optimal combination of hyperparameters. This may take 30 seconds or longer to run on your machine.

```
grid.best_params_
```

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