Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

KNN Settings

K-NN

K-Nearest Neighbors regressor

K (Neighbors)5

1 = high variance50 = high bias

Number of neighbors to average. Lower K captures local patterns; higher K smooths predictions.

Weights

Uniform: all K neighbors weighted equally. Distance: closer neighbors have more influence.

Distance Metric

Generalised distance controlled by p. p=1 → Manhattan, p=2 → Euclidean, p→∞ → Chebyshev.

Minkowski p2

1 = Manhattan2 = Euclidean

Search Algorithm▼

Tree algorithm & leaf size

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

KNN is distance-based — standard scaling is strongly recommended so no single feature dominates.

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

▼

Performance Metrics

R²

—

Awaiting training

RMSE

—

Awaiting training

MAE

—

Awaiting training

Adj. R²

—

Awaiting training

Train R²

—

Awaiting training

K Neighbors

—

Awaiting training

Avg Neighbor Dist

—

Awaiting training

Samples

—

Awaiting training

Actual vs Predicted

Train the model to see actual vs predicted scatter

Advanced Analysis

Deep Dive Analysis

Run training to populate analysis

Run training to populate this tab

Job History

Recent jobs — auto-pruned to keep 10 per type

No jobs recorded yet. Run a clustering, classification, or pipeline job to see results here.

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Dataset

Upload CSV or Excel with a numeric target

Try with a sample dataset

Pick one to explore instantly — no upload needed

KNN Settings

K-NN

K-Nearest Neighbors regressor

K (Neighbors)5

1 = high variance50 = high bias

Number of neighbors to average. Lower K captures local patterns; higher K smooths predictions.

Weights

Uniform: all K neighbors weighted equally. Distance: closer neighbors have more influence.

Distance Metric

Generalised distance controlled by p. p=1 → Manhattan, p=2 → Euclidean, p→∞ → Chebyshev.

Minkowski p2

1 = Manhattan2 = Euclidean

Search Algorithm▼

Tree algorithm & leaf size

Train / Test Split

Holdout proportion for evaluation

Test Size20%

Train: 80%Test: 20%

Preprocessing

Feature transformations

Enable preprocessing

Advanced settings

KNN is distance-based — standard scaling is strongly recommended so no single feature dominates.

Projection

2D visualization method

PCA for quick linear projection. UMAP for complex manifolds.

Parameter Guide

▼

Performance Metrics

R²

—

Awaiting training

RMSE

—

Awaiting training

MAE

—

Awaiting training

Adj. R²

—

Awaiting training

Train R²

—

Awaiting training

K Neighbors

—

Awaiting training

Avg Neighbor Dist

—

Awaiting training

Samples

—

Awaiting training

Actual vs Predicted

Train the model to see actual vs predicted scatter

Advanced Analysis

Deep Dive Analysis

Run training to populate analysis

Run training to populate this tab

Job History

Recent jobs — auto-pruned to keep 10 per type

No jobs recorded yet. Run a clustering, classification, or pipeline job to see results here.

KNN Regressor — Interactive Analysis