refer to code:
The code you provided is a Python script that uses the MLflow library to perform K-means clustering on the Iris dataset, logs the model parameters and metrics, and identifies the best K value (number of clusters) based on the Adjusted Rand Index (ARI) and Silhouette Score. It also generates a histogram plot of the predicted labels for the best K value and saves it as an artifact.
Here's an overview of the code:
Import the necessary libraries, including MLflow, scikit-learn, and matplotlib.
Create a new MLflow experiment called "iris_experiment" if it doesn't already exist.
Start an MLflow run in the context of the "iris_experiment".
Load the Iris dataset and log its shape as a dictionary.
Iterate through different K values (number of clusters) from 2 to 9 and perform the following steps:
a. Instantiate and fit a KMeans model with the current K value.
b. Obtain the predicted cluster assignments and compare them with the true labels using ARI and Silhouette Score.
c. Log the ARI and Silhouette Score metrics for each K value using MLflow.
d. Keep track of the best K value based on the average of ARI and Silhouette Score.
Print the best K value and its corresponding score.
Create a histogram plot of the predicted labels for the best K value and save it as an artifact using MLflow.