How to Track Experiments in Machine Learning Effectively

To track experiments in machine learning, use experiment tracking tools like MLflow or Weights & Biases that log parameters, metrics, and models automatically. This helps you compare results and reproduce experiments easily.

Syntax

Experiment tracking typically involves logging parameters, metrics, and artifacts during model training. For example, with MLflow, you start a run, log parameters and metrics, then end the run.

• mlflow.start_run(): Begins an experiment run.
• mlflow.log_param(name, value): Logs a parameter like learning rate.
• mlflow.log_metric(name, value): Logs a metric like accuracy.
• mlflow.end_run(): Ends the current run.

python

import mlflow

mlflow.start_run()
mlflow.log_param('learning_rate', 0.01)
mlflow.log_metric('accuracy', 0.95)
mlflow.end_run()

Example

This example shows how to track a simple model training experiment using MLflow. It logs the learning rate and accuracy so you can review them later.

python

import mlflow
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)

# Start experiment tracking
mlflow.start_run()

# Set and log parameter
learning_rate = 0.1
mlflow.log_param('learning_rate', learning_rate)

# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict and calculate accuracy
preds = model.predict(X_test)
acc = accuracy_score(y_test, preds)

# Log metric
mlflow.log_metric('accuracy', acc)

# End run
mlflow.end_run()

print(f'Logged experiment with accuracy: {acc:.2f}')

Output

Logged experiment with accuracy: 1.00

Common Pitfalls

Common mistakes when tracking experiments include:

• Not logging all important parameters, making it hard to reproduce results.
• Forgetting to end runs, which can cause confusion in tracking tools.
• Logging inconsistent metric names or types, which complicates comparison.
• Not saving model artifacts, losing the ability to deploy or test models later.

python

import mlflow

# Wrong way: forgetting to end run and inconsistent metric names
mlflow.start_run()
mlflow.log_param('lr', 0.01)
mlflow.log_metric('acc', 0.9)
# Missing mlflow.end_run()

# Right way:
mlflow.end_run()
mlflow.start_run()
mlflow.log_param('learning_rate', 0.01)
mlflow.log_metric('accuracy', 0.9)
mlflow.end_run()

Quick Reference

Action	Function	Description
Start experiment	mlflow.start_run()	Begin tracking a new experiment run
Log parameter	mlflow.log_param(name, value)	Record a model or training parameter
Log metric	mlflow.log_metric(name, value)	Record a performance metric
Log artifact	mlflow.log_artifact(file_path)	Save files like models or plots
End experiment	mlflow.end_run()	Finish the current experiment run

Key Takeaways

Use experiment tracking tools like MLflow to log parameters, metrics, and models automatically.

Always log all relevant parameters and metrics consistently for easy comparison and reproduction.

Remember to start and end experiment runs properly to keep tracking organized.

Save model artifacts to reuse or deploy your trained models later.

Review logged experiments regularly to choose the best performing model.