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Documentation Index

Fetch the complete documentation index at: https://docs.clickml.app/llms.txt

Use this file to discover all available pages before exploring further.

This guide walks you through a minimal end-to-end pipeline: load a CSV, split the data, train a Random Forest, and evaluate it.

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1. Create a project

  1. Sign in at app.clickml.com.
  2. Click New Project.
  3. Give it a name, select a problem type (Classification or Regression), and confirm.
You land on the canvas — a blank workspace with a toolbox on the left.

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2. Add a CSV Reader

Drag the CSV Reader component onto the canvas. Click it to open the inspector panel. Upload your CSV file (or paste a URL). Make sure your file has a header row.

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3. Add a Train-Test Split

Drag Train-Test Split onto the canvas, to the right of CSV Reader. Connect the CSV Reader’s output handle to the Train-Test Split’s input handle by dragging between the two dots. In the inspector, set your target column (the column you want to predict) and adjust the test size if needed (default: 20%).

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4. Add a model

Drag Random Forest Classifier (or Random Forest Regressor for regression problems) onto the canvas. Connect the X Train and Y Train output handles from Train-Test Split to the model’s input.

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5. Add Inference

Drag the Inference component onto the canvas. Connect the model output and the X Test handle from Train-Test Split to Inference’s inputs.

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6. Add Evaluation

Drag Evaluation onto the canvas. Connect Y Test from Train-Test Split and the predictions output from Inference to Evaluation’s inputs. Set the problem type in Evaluation’s inspector to match your project.

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7. Run

Click Run in the top bar. The pipeline executes and each component shows a status indicator. Open Evaluation to see your metrics: accuracy, F1, confusion matrix (classification) or MAE, RMSE, R² (regression).

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Minimal pipeline diagram

CSV Reader → Train-Test Split → Random Forest → Inference → Evaluation
From here you can add preprocessing steps (Scaling, Encoding, Missing Values) between the CSV Reader and the split, or swap in a different model to compare results.