basedLineF1Score = f.ScoreDataFrame(names, results, 'baseline_f1_Score')
models = f.GetBasedModel()
names, results = f.get_model_performance(X_train, y_train, models, SEED,
'accuracy')
f.PlotBoxR().PlotResult(names, results)
basedLineAccuracyScore = f.ScoreDataFrame(names, results, 'baseline_accuracy')
# Record Scores
ScoreCard = pd.concat([basedLineAccuracyScore, basedLineF1Score], axis=1)
# Scaled Model on top of baseline
# Standard Scalar
models = f.GetScaledModel('standard')
names, results = f.get_model_performance(X_train, y_train, models, SEED,
'f1_weighted')
#f.PlotBoxR().PlotResult(names,results)
# Record Scores
scaledScoreStandard = f.ScoreDataFrame(names, results, 'standard_f1_score')
ScoreCard = pd.concat([ScoreCard, scaledScoreStandard], axis=1)
# Minmax scalar
models = f.GetScaledModel('minmax')
names, results = f.get_model_performance(X_train, y_train, models, SEED,
'f1_weighted')
f.PlotBoxR().PlotResult(names, results)
# Record Scores
def scaled_model(self, scoring, SEED, result_col_nm, scalar):
models = f.GetScaledModel(scalar)
names, results = f.get_model_performance(self.X_train, self.y_train,
models, SEED, scoring)
_score = f.ScoreDataFrame(names, results, result_col_nm)
return _score