Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

# 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 #key# SVC: Support Vector Classifier #key#, we will focus on C and gamma, which generally have the biggest impact on prediction accuracy when using this algorithm.

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.
```python
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.

```python
grid = GridSearchCV(SVC(),hyperparameters)
```
Next, fit grid search to the X and y training data.
```python
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.
```python
grid.best_params_
```

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

SVM Implementation Steps: 8 and 9

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Customer Segmentation with K-Means Clustering

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

SVM Implementation Steps: 8 and 9

8) Grid search