# Models

## Getting Started

In this section, we will explore various models offered by the Spice Cloud Platform.

### Model Types
- **Classification Models**: Used to categorize data into predefined classes.
- **Regression Models**: Used for predicting continuous values.
- **Clustering Models**: Used for grouping similar data points.

### Example of a Classification Model
```python
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier

# Load dataset
iris = datasets.load_iris()
X, y = iris.data, iris.target

# Create a classifier
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X, y)
```

## Advanced Features

### Hyperparameter Tuning
- Use techniques like Grid Search or Random Search to optimize model performance.

### Model Evaluation Metrics
- **Accuracy**: Ratio of correct predictions to total predictions.
- **F1 Score**: Balance between precision and recall.