ml pack

20 nodes, in noodlelab[ml] and in the tiers that build on it.

Machine Learning/Data

Practice Data

ml.practice_data

A made-up table to learn on, drawn from a known rule plus noise. beams: a beam’s deflection (mm) from its load (kN) and length (mm), close to a straight line: for regression. welds: welding settings and whether the weld passed inspection; it fails when the heat per millimetre of plate is too low or too high: for classification. machines: sensor readings from machines in three operating modes, with 2% faults mixed in and no labels: for principal components, clustering and anomaly detection.

Inputs

Name	Type	Description
dataset	Literal['beams', 'welds', 'machines']	Default 'beams'.
rows	int	Default 500.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
result	DataFrame

Train/Test Split

ml.train_test_split

Rows chosen at random (with the seed) for testing, the rest for training. Judge a model only on rows it never saw: a score on its own training rows flatters it. group keeps repeats of one thing on one side, so the test is not passed by recognising them. The split keeps the tables’ units and descriptions.

Inputs

Name	Type	Description
table	DataFrame
test_share	float	Share of rows held out Default 0.25.
stratify	str	Keep this column’s classes in proportion; empty: no Default ''.
group	str	Rows with the same value go to the same side (a specimen, a site) Default ''.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
train	DataFrame
test	DataFrame

Machine Learning/Evaluate

Cross-Validate

ml.cross_validate

Refit the model on all but one fold of the rows and score it on that fold, for each fold in turn: a steadier judgement than one split. The model is refitted exactly as it was fitted (same settings, preprocessing and early stopping), and its own fit is left as it is.

Inputs

Name	Type	Description
model	Model
table	DataFrame
folds	int	Parts the rows are split into Default 5.
metric	Literal['auto', 'rmse', 'mae', 'r2', 'accuracy', 'f1']	Default 'auto'.
group	str	Rows with the same value stay in one fold (e.g. a specimen); empty: none Default ''.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
score	Uncertain
folds	DataFrame
summary	dict[str, Any]

Evaluate Model

ml.evaluate_model

How well a model predicts a table it was not trained on (the test part of Train/Test Split): for a regressor the RMSE and MAE in the target’s unit, R², and a plot of predicted against true values; for a classifier the accuracy, the F1 score (the mean of each class’s F1, so a rare class counts as much as a common one), and the confusion matrix. Metrics that do not apply are None (NaN would say something went wrong).

Inputs

Name	Type	Description
model	Model
table	DataFrame

Outputs

Name	Type	Description
rmse	Any
mae	Any
r_squared	float | None
accuracy	float | None
f1	float | None
confusion	DataFrame
chart	Figure
summary	dict[str, Any]

Learning Curve

ml.learning_curve

The cross-validated score for growing shares of the training rows: whether more data would help (the validation score still rising) or the model is as good as it gets (the two curves have met).

Inputs

Name	Type	Description
model	Model
table	DataFrame
folds	int	Parts the rows are split into Default 5.
steps	int	Training sizes to try Default 6.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
table	DataFrame
chart	Figure

Permutation Importance

ml.permutation_importance

How much worse the model predicts a table when one feature’s values are shuffled, breaking its link to the target: the drop in R² (or accuracy), mean and standard deviation over repeats shuffles. Works for every model and on the original columns (a text column counts once), unlike a tree’s own importance. Use a held-out table, or it measures what the model memorised.

Inputs

Name	Type	Description
model	Model
table	DataFrame
repeats	int	Default 10.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
table	DataFrame
chart	Figure

Predict

ml.predict

The model’s predictions for each row of a table with the feature columns (the target column is not needed). Columns in other units of the same dimension are converted to the ones the model was trained on.

Inputs

Name	Type	Description
model	Model
table	DataFrame

Outputs

Name	Type	Description
table	DataFrame
values	Any
probabilities	DataFrame

Machine Learning/Models

Gaussian Process

ml.gaussian_process

A Gaussian process regressor: a smooth model that knows how sure it is. Predict Values gives each prediction with its standard uncertainty (as an uncertain value, correlated between nearby points), which grows away from the training data. The kernel is a constant times an RBF with one length scale per feature (so it learns which features matter), plus white noise when noise is on. Suited to surrogate models of a few hundred to a few thousand runs; the fit grows with the cube of the rows.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
noise	bool	The target has scatter (measurements), not exact values Default True.
restarts	int	Extra starts of the kernel fit Default 2.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
model	Model
score	float
summary	dict[str, Any]

Gradient Boosting

ml.gradient_boosting

scikit-learn’s histogram gradient boosting: trees added one at a time, each correcting the ones before. Takes gaps in the data as they are. See also the XGBoost and LightGBM nodes.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
task	Literal['regression', 'classification']	Predict a number, or a category Default 'regression'.
iterations	int	Default 200.
learning_rate	float	Default 0.1.
max_depth	int	Deepest tree; 0: no limit Default 0.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
model	Model
score	float
summary	dict[str, Any]

LightGBM

ml.lightgbm

Gradient-boosted trees with LightGBM, which grows each tree leaf by leaf: fast on large tables. The outputs are those of the XGBoost node.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
task	Literal['regression', 'classification']	Predict a number, or a category Default 'regression'.
trees	int	Most trees to add Default 500.
learning_rate	float	Shrinks each tree Default 0.05.
leaves	int	Most leaves per tree Default 31.
subsample	float	Share of rows each tree sees Default 1.0.
early_stopping	int	Stop after this many trees without improvement; 0: off Default 20.
validation	float	Share of rows held back to stop on Default 0.2.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
model	Model
score	float
importance	DataFrame
best_iteration	int
summary	dict[str, Any]

Linear Model

ml.linear_model

A straight-line (linear) model of a number: ordinary least squares, ridge (shrinks the coefficients: steadier with correlated features) or lasso (sets some to zero: picks features). strength is the penalty of ridge and lasso, on standardised features.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
method	Literal['least squares', 'ridge', 'lasso']	Default 'least squares'.
strength	float	Regularisation: larger is simpler Default 1.0.

Outputs

Name	Type	Description
model	Model
score	float
summary	dict[str, Any]

Logistic Regression

ml.logistic_regression

A linear classifier: the probability of each class from a weighted sum of the (standardised) features. strength is the L2 penalty (1/C).

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
strength	float	Regularisation: larger is simpler Default 1.0.

Outputs

Name	Type	Description
model	Model
score	float
summary	dict[str, Any]

Random Forest

ml.random_forest

Many decision trees, each on a bootstrap sample of the rows, averaged (or voting). Robust with little tuning, and needs no scaling.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
task	Literal['regression', 'classification']	Predict a number, or a category Default 'regression'.
trees	int	Default 200.
max_depth	int	Deepest tree; 0: no limit Default 0.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
model	Model
score	float
summary	dict[str, Any]

Support Vector Machine

ml.support_vector_machine

A support vector machine on the standardised features; the rbf kernel draws curved boundaries. strength is the penalty (1/C). Slow beyond some ten thousand rows.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
task	Literal['regression', 'classification']	Predict a number, or a category Default 'regression'.
kernel	Literal['rbf', 'linear', 'poly']	Default 'rbf'.
strength	float	Regularisation: larger is simpler Default 1.0.

Outputs

Name	Type	Description
model	Model
score	float
summary	dict[str, Any]

XGBoost

ml.xgboost

Gradient-boosted trees with XGBoost. Gives the model, its training score, each feature’s importance (its share of the total gain: how much the splits on it improved the fit; plot it with Bar Chart) and the number of trees kept. There is no chart among the outputs, so the fitted model can be checkpointed without pickle.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
task	Literal['regression', 'classification']	Predict a number, or a category Default 'regression'.
trees	int	Most trees to add Default 500.
learning_rate	float	Shrinks each tree Default 0.05.
max_depth	int	Default 6.
subsample	float	Share of rows each tree sees Default 1.0.
early_stopping	int	Stop after this many trees without improvement; 0: off Default 20.
validation	float	Share of rows held back to stop on Default 0.2.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
model	Model
score	float
importance	DataFrame
best_iteration	int
summary	dict[str, Any]

k-Nearest Neighbours

ml.k_nearest_neighbours

Predicts from the neighbours training rows nearest in the (standardised) features: their mean, or their most common class. distance weights nearer rows more.

Inputs

Name	Type	Description
table	DataFrame
target	str	The column to predict Default ''.
features	str	Columns to learn from, comma-separated; empty: every column but the target Default ''.
task	Literal['regression', 'classification']	Predict a number, or a category Default 'regression'.
neighbours	int	Default 5.
weights	Literal['uniform', 'distance']	Default 'uniform'.

Outputs

Name	Type	Description
model	Model
score	float
summary	dict[str, Any]

Machine Learning/Uncertainty

Prediction Interval

ml.prediction_interval

Predictions with an interval that holds the true value with the chosen probability, for any regressor (split conformal prediction). Calibrate on rows the model was not trained on, with the target column: the test part of a Train/Test Split, say.

Inputs

Name	Type	Description
model	Model
calibration	DataFrame
table	DataFrame
coverage	float	Share of true values inside Default 0.9.

Outputs

Name	Type	Description
table	DataFrame
values	Any
half_width	Any
summary	dict[str, Any]

Machine Learning/Unsupervised

Cluster

ml.cluster

Groups of similar rows, as a cluster column. k-means finds clusters round groups; DBSCAN finds dense regions of any shape and marks rows in none as -1 (noise). silhouette (−1 to 1) says how well separated the groups are; NaN with fewer than two groups.

Inputs

Name	Type	Description
table	DataFrame
features	str	Columns to use, comma-separated; empty: every numeric one Default ''.
method	Literal['k-means', 'dbscan']	Default 'k-means'.
clusters	int	k-means: how many Default 3.
radius	float	DBSCAN: neighbourhood size Default 0.5.
min_rows	int	DBSCAN: rows to make a core Default 5.
scale	bool	Standardise each column first, so units do not set the weights Default True.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
table	DataFrame
counts	DataFrame
silhouette	float

Detect Anomalies

ml.detect_anomalies

Rows unlike the rest, judged on several columns at once: a reading can be ordinary in each column and still odd in combination, which Detect Outliers (one column at a time) misses. An isolation forest counts how few random cuts set a row apart; the local outlier factor compares how crowded a row’s neighbourhood is with its neighbours’. share is how many rows to flag. flagged gains anomaly and anomaly_score (higher is odder); clean is the table without them.

Inputs

Name	Type	Description
table	DataFrame
features	str	Columns to use, comma-separated; empty: every numeric one Default ''.
method	Literal['isolation forest', 'local outlier factor']	Default 'isolation forest'.
share	float	The share of rows you expect to be odd Default 0.02.
scale	bool	Standardise each column first, so units do not set the weights Default True.
seed	int	The same seed gives the same model Default 0.

Outputs

Name	Type	Description
flagged	DataFrame
clean	DataFrame
count	int

Principal Components

ml.principal_components

Principal component analysis: the directions along which the rows vary most. table gains columns PC1, PC2…; explained gives each component’s share of the variance; loadings how much each column contributes to each component.

Inputs

Name	Type	Description
table	DataFrame
features	str	Columns to use, comma-separated; empty: every numeric one Default ''.
components	int	Default 2.
scale	bool	Standardise each column first, so units do not set the weights Default True.

Outputs

Name	Type	Description
table	DataFrame
explained	DataFrame
loadings	DataFrame
chart	Figure