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

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.