ml-evaluator 1.0.2

Model Evaluation Toolkit — bias-variance analysis, ROC curves, model summary & multi-model comparison

ml-evaluator

Stop copy-pasting evaluation code.

pip install ml-evaluator

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What is it?

ml-evaluator turns model evaluation from a chore into a single function call.

Every function is standalone — pass your model, X_test, and y_test and you're done. No pipelines to build, no intermediate objects to store, no boilerplate to copy.

Built for people who:

• know how to train a model but want clean, reproducible evaluation
• are learning ML and want plots + plain-English interpretation, not just numbers
• are tired of writing the same confusion-matrix / ROC / bias-variance code every project

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Install

pip install ml-evaluator

import ml_evaluator as ev

Requirements: Python 3.8+ · numpy · pandas · matplotlib · scikit-learn

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API

Every piece of evaluation is its own function — use exactly what you need.

🔹 Single model — text output

ev.metrics(model, X_test, y_test)           # numbers only, no plot
ev.interpret(model, X_test, y_test)         # plain-English interpretation, no plot
ev.classification_report(model, X_test, y_test)  # classification report table, no plot

Example output — ev.metrics()

=======================================================
  Model Summary — Random Forest
=======================================================
  Accuracy  : 0.9625
  F1        : 0.9634
  Precision : 0.9518
  Recall    : 0.9753
  ROC-AUC   : 0.9930

Example output — ev.interpret()

  Interpretation — Random Forest
  ─────────────────────────────────────────────────────
  ✅  Accuracy 0.963 — high overall correctness.
  ✅  F1 0.963 — strong balance between precision and recall.
  ✅  Precision (0.952) ≈ Recall (0.975) — well balanced.
       AUC = 0.993 — Excellent. The model clearly separates the two classes.

Example output — ev.classification_report()

  Classification Report — Random Forest
  ────────────────────────────────────────────────────────
  Class             Precision   Recall  F1-Score  Support
  ────────────────────────────────────────────────────────
  0                     0.970    0.952     0.961      100
  1                     0.953    0.971     0.962      100
  ────────────────────────────────────────────────────────
  Accuracy                                 0.963      200
  macro avg             0.961    0.962     0.961
  weighted avg          0.961    0.962     0.961

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🔹 Single model — individual plots

ev.plot_confusion_matrix(model, X_test, y_test)   # confusion matrix only
ev.plot_roc_curve(model, X_test, y_test)           # ROC curve only
ev.plot_metrics_bar(model, X_test, y_test)         # metrics bar chart only

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🔹 Single model — all-in-one

ev.model_summary(model, X_test, y_test)

Produces a 2×2 dashboard in one figure:

┌─────────────────────┬─────────────────────┐
│  A · Confusion      │  B · ROC Curve      │
│      Matrix         │                     │
├─────────────────────┼─────────────────────┤
│  C · Metrics        │  D · Classification │
│      Bar Chart      │      Report         │
└─────────────────────┴─────────────────────┘

Also prints metrics + interpretation to the terminal.

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🔸 Bias–Variance — single model

ev.bv_stats(model, X_train, y_train)          # stats + diagnosis only, no plot
ev.plot_learning_curve(model, X_train, y_train)   # learning curve plot only
ev.bias_variance(model, X_train, y_train)     # stats + plot together

Example output — ev.bv_stats()

  ✅ Random Forest
     Train Acc : 1.0000
     Val   Acc : 0.9359
     Gap       : 0.0641   (threshold: 0.10)
     Val Std   : 0.0167   (variance proxy)
     Diagnosis : Good Fit
     Train–val gap is 0.064 and val error is 0.064.
     The model generalises well — no strong signs of overfit or underfit.
     → Next step: evaluate on the held-out test set.

Diagnosis logic:

Condition	Diagnosis	What it means
train − val gap > 0.10	🔴 Overfit	Model memorises training data, fails to generalise
1 − val_accuracy > 0.15	🟡 Underfit	Model too simple to capture the pattern
otherwise	✅ Good Fit	Model generalises well

Thresholds are configurable:

ev.bias_variance(
    model, X_train, y_train,
    overfit_threshold=0.05,      # stricter
    high_bias_threshold=0.10,
)

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🔸 Multiple models — text output

ev.compare_metrics(models, X_test, y_test)     # metrics table + winner per metric, no plot
ev.compare_interpret(models, X_test, y_test)   # interpretation per model, no plot

Example output — ev.compare_metrics()

=================================================================
  Model Comparison
=================================================================
  Model                   Accuracy        F1 Precision    Recall   ROC-AUC
  ──────────────────────────────────────────────────────────────
  Random Forest             0.9625    0.9634    0.9518    0.9753    0.9930
  Logistic Regression       0.9375    0.9412    0.8989    0.9877    0.9833

  Winners by metric:
    Accuracy     → Random Forest        (0.9625)
    F1           → Random Forest        (0.9634)
    Precision    → Random Forest        (0.9518)
    Recall       → Logistic Regression  (0.9877)
    ROC-AUC      → Random Forest        (0.9930)

  ✅  Overall recommendation: Random Forest
      (weighted score — F1 & Recall weighted highest)

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🔸 Multiple models — individual plots

ev.plot_confusion_matrices(models, X_test, y_test)   # one matrix per model
ev.compare_roc_curves(models, X_test, y_test)         # overlaid ROC curves
ev.plot_metrics_comparison(models, X_test, y_test)    # grouped bar chart

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🔸 Multiple models — all-in-one shortcuts

ev.comparison_dashboard(models, X_test, y_test)
ev.compare_bias_variance(models, X_train, y_train)

comparison_dashboard — full 3-row dashboard:

Row 1 — Confusion matrix per model
Row 2 — Overlaid ROC curves  +  Metrics bar chart
Row 3 — Colour-coded summary table (green = top, red = bottom)

compare_bias_variance — two-row B-V dashboard:

Row 1 — Learning curve per model with diagnosis label
Row 2 — Bias proxy  ·  Variance  ·  Overfitting gap comparison

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All parameters

Single model functions

ev.metrics(model, X_test, y_test, model_name="Model", verbose=True)
ev.interpret(model, X_test, y_test, model_name="Model", verbose=True)
ev.classification_report(model, X_test, y_test, model_name="Model", class_labels=None)

ev.plot_confusion_matrix(model, X_test, y_test,
    model_name="Model",
    class_labels=None,       # e.g. ["Not Churned", "Churned"]
    color="#2E75B6",
    figsize=(5, 4.5),
    save_path=None,
)

ev.plot_roc_curve(model, X_test, y_test,
    model_name="Model",
    color="#2E75B6",
    figsize=(6, 5),
    save_path=None,
)

ev.plot_metrics_bar(model, X_test, y_test,
    model_name="Model",
    color="#2E75B6",
    figsize=(7, 4),
    save_path=None,
)

ev.model_summary(model, X_test, y_test,
    model_name="Model",
    class_labels=None,
    color="#2E75B6",
    figsize=(14, 10),
    save_path=None,
)

Bias–Variance functions

ev.bv_stats(model, X_train, y_train,
    model_name="Model",
    n_splits=5,
    random_state=42,
    train_sizes=None,            # default: linspace(0.1, 1.0, 8)
    scoring="accuracy",
    overfit_threshold=0.10,
    high_bias_threshold=0.15,
    verbose=True,
)

ev.plot_learning_curve(model, X_train, y_train,
    model_name="Model",
    n_splits=5,
    random_state=42,
    train_sizes=None,
    scoring="accuracy",
    overfit_threshold=0.10,
    high_bias_threshold=0.15,
    color="#2E75B6",
    figsize=(8, 5),
    save_path=None,
)

# bias_variance() accepts all parameters above

Multi-model functions

# All multi-model functions accept:
models    = {"name": fitted_estimator, ...}   # required
colors    = None     # list of colours, one per model
figsize   = None     # auto-sized if not given
save_path = None     # saves figure to this path

# compare_metrics / compare_interpret also accept:
verbose   = True

# plot_confusion_matrices / comparison_dashboard also accept:
class_labels = None  # e.g. ["No", "Yes"]

# compare_bias_variance also accepts all bv_stats parameters

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Return values

Function	Returns
metrics()	dict — accuracy, f1, precision, recall, roc_auc, y_pred, y_prob, report
interpret()	str — full interpretation text
bv_stats()	dict — lc_sizes, lc_train, lc_val, mean_train, mean_val, val_std, gap, bias_proxy, diagnosis, explanation
compare_metrics()	pandas.DataFrame — one row per model
compare_interpret()	dict — {model_name: interpretation_string}
all plot_* functions	None
model_summary()	None
comparison_dashboard()	None
bias_variance()	None
compare_bias_variance()	None

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Roadmap

• v1.2 — Threshold optimiser (best decision threshold for F1 / Recall)
• v1.3 — Probability calibration plot (reliability diagram)
• v1.4 — Feature importance comparison across models
• v2.0 — Auto PDF report

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Contributing

Issues and pull requests are welcome.

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License

MIT