AutoML Model Training with MLJAR

Train machine learning models easily with MLJAR AutoML! Load your data, pick features and target, start training, and explore clear reports - with little or no coding skills needed.

In this notebook, you’ll smoothly navigate four key steps to take your data from raw file to trained models and reports - no deep coding required. MLJAR’s AutoML engine will automatically test multiple algorithms, optimize hyperparameters, and generate interactive performance dashboards for you.

Steps:

  1. Load your dataset

  2. Define features and target

  3. Train with MLJAR AutoML

  4. Review the comprehensive report

Let’s get started!

Step 1 – Load Your Dataset 📥

Bring your data into the notebook easily:

  • 🍰 Recipe: Read CSV - Upload a CSV using pd.read_csv('file.csv').

  • 🤖 AI Read File - AI auto-detects and loads your file into a DataFrame named df.

After loading, you’ll immediately see the first few rows to verify everything loaded correctly.

Step 2 – Define Features (X) & Target (y) 🎯

Prepare your modeling data:

  • 🔧 Select X, y - Manually specify X = df[['feat1','feat2']] and y = df['target'].

  • 🤖 Ask AI: Split into X and y - Have AI choose columns or confirm your selection and perform the split.

Once done, you’ll have X (predictors) and y (outcome) ready for training.

Step 3 – Train with MLJAR AutoML 🤖⚙️

Launch automated model training:

  • Select Features (X) and Target (y) in the AutoML UI.

  • Confirm to start MLJAR AutoML.

Watch logs as multiple models are trained and validated under the hood.

Step 4 – Review AutoML Report 📑✨

Explore a rich, interactive report:

  • Model Leaderboard: See all models ranked by your chosen metric.

  • Performance Metrics: Dive into accuracy, AUC, MSE, and more.

  • Visualizations: Feature importance charts, ROC curves, and learning curves.

Use the built-in report viewer or ask AI to load the HTML within the notebook.

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