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:
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Load your dataset
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Define features and target
-
Train with MLJAR AutoML
-
Review the comprehensive report
Let’s get started!
Step 1 – Load Your Dataset 📥
Bring your data into the notebook easily:
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🍰 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']]
andy = 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:
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Select Features (X) and Target (y) in the AutoML UI.
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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:
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Model Leaderboard: See all models ranked by your chosen metric.
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Performance Metrics: Dive into accuracy, AUC, MSE, and more.
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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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