b1de080823e41b921bec2949db2b6c3cb1f1d5ef,examples/plot_feature_rep.py,,,#,30

Before Change

y = data["y"]

// create a feature representation pipeline
feed = Pipeline([("segment", Segment()),
                 ("features", SegFeatures(features = base_features()))])
est = Pipeline([("scaler", StandardScaler()),
                ("rf", RandomForestClassifier())])
pipe = SegPipe(feed, est)

After Change

print("Accuracy score: ", score)

// now lets add some contextual data
Xc = np.column_stack((data["side"], data["subject"]))
X = make_ts_data(data["X"], Xc)
y = data["y"]

// and do a cross validation
scoring = make_scorer(f1_score, average = "macro")

Instances