FIGS Regression (California Housing)

Experiment initialization and data preparation

from piml import Experiment
from piml.models import FIGSRegressor

exp = Experiment()
exp.data_loader(data="CaliforniaHousing_trim2", silent=True)
exp.data_prepare(target="MedHouseVal", task_type="regression", silent=True)

Train Model

exp.model_train(model=FIGSRegressor(max_iter=100, max_depth=4), name="FIGS")

Evaluate predictive performance

exp.model_diagnose(model="FIGS", show="accuracy_table")

          MSE     MAE       R2

Train  0.0103  0.0705   0.8196
Test   0.0114  0.0739   0.7941
Gap    0.0012  0.0034  -0.0256

Global interpretation for the splits heatmap

exp.model_interpret(model="FIGS", show="figs_heatmap", tree_idx=0, figsize=(12, 4))

Global interpretation for the first tree

exp.model_interpret(model="FIGS", show="tree_global", root=0, tree_idx=0,
                    depth=3, original_scale=True, figsize=(16, 10))

Global interpretation for the second tree

exp.model_interpret(model="FIGS", show="tree_global", root=0, tree_idx=1,
                    depth=3, original_scale=True, figsize=(16, 10))

Local interpretation for the first tree

exp.model_interpret(model="FIGS", show="tree_local", sample_id=0, tree_idx=0,
                    original_scale=True, figsize=(16, 10))

Local interpretation for the second tree

exp.model_interpret(model="FIGS", show="tree_local", sample_id=0, tree_idx=1,
                    original_scale=True, figsize=(16, 10))

Estimated memory usage: 35 MB