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scicloj.metamorph.ml.regression

Regression models for continuous target prediction.

This namespace provides implementations of various regression algorithms with a consistent metamorph.ml training and prediction interface. Models support statistical output formats (tidy, glance, augment) for analysis and diagnostics.

Available Models:

OLS (Ordinary Least Squares)

  • :metamorph.ml/ols: Apache Commons Math implementation (Java-based)
  • :fastmath/ols: FastMath implementation (pure Clojure) Solves for regression coefficients β in: y = Xβ + ε Assumes linear relationships and homoscedastic errors.

GLM (Generalized Linear Model)

  • :fastmath/glm: FastMath GLM implementation Extends linear regression to non-normal distributions and non-linear relationships via link functions and variance models.

Baseline Model

  • :metamorph.ml/dummy-regressor: Predicts mean of training target Useful sanity check - models should outperform this baseline.

Model Output Functions:

  • :tidy-fn: Extracts model coefficients with statistics Returns dataset with :term, :estimate, :std.error, :statistic, :p.value
  • :glance-fn: Extracts model-level diagnostics Returns dataset with :r.squared, :adj.r.squared, :rss, :aic, :bic, etc.
  • :augment-fn: Adds model predictions and residuals to data Returns augmented dataset with :.fitted and :.resid columns

Example Usage (in metamorph pipeline): (ml/train data {:model-type :fastmath/ols :target-columns [:price] :feature-columns [:sqft :bedrooms]})

Model Diagnostics: (glance model) ; Overall model metrics (tidy model) ; Coefficient table (augment model ds) ; Predicted values and residuals

See also: scicloj.metamorph.ml.r-model-matrix for formula-based feature engineering

Regression models for continuous target prediction.

This namespace provides implementations of various regression algorithms with
a consistent metamorph.ml training and prediction interface. Models support
statistical output formats (tidy, glance, augment) for analysis and diagnostics.

Available Models:

**OLS (Ordinary Least Squares)**
- `:metamorph.ml/ols`: Apache Commons Math implementation (Java-based)
- `:fastmath/ols`: FastMath implementation (pure Clojure)
Solves for regression coefficients β in: y = Xβ + ε
Assumes linear relationships and homoscedastic errors.

**GLM (Generalized Linear Model)**
- `:fastmath/glm`: FastMath GLM implementation
Extends linear regression to non-normal distributions and non-linear relationships
via link functions and variance models.

**Baseline Model**
- `:metamorph.ml/dummy-regressor`: Predicts mean of training target
Useful sanity check - models should outperform this baseline.

Model Output Functions:

- **:tidy-fn**: Extracts model coefficients with statistics
  Returns dataset with :term, :estimate, :std.error, :statistic, :p.value
- **:glance-fn**: Extracts model-level diagnostics
  Returns dataset with :r.squared, :adj.r.squared, :rss, :aic, :bic, etc.
- **:augment-fn**: Adds model predictions and residuals to data
  Returns augmented dataset with :.fitted and :.resid columns

Example Usage (in metamorph pipeline):
(ml/train
  data
  {:model-type :fastmath/ols
   :target-columns [:price]
   :feature-columns [:sqft :bedrooms]})

Model Diagnostics:
(glance model)      ; Overall model metrics
(tidy model)        ; Coefficient table
(augment model ds)  ; Predicted values and residuals

See also: `scicloj.metamorph.ml.r-model-matrix` for formula-based feature engineering
raw docstring

predict-fmclj

(predict-fm feature-ds thawed-model model)
source

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