ubcv-grade-predictor-ridge

adrian-lam/ubcv-grade-predictor-ridge

ubcv-grade-predictor-ridge is a tabular regression model from adrian-lam. Use it for the tabular regression task on the model card, and read the license before you ship it in a product. It is set up for sklearn.

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Updated Dec 20, 2024

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At a glance

Task: Tabular Regression
Library: sklearn
Access: Public
Created: Dec 20, 2024
Updated: Dec 20, 2024
SHA: 7ecf383a

Model card

From the Hugging Face model README

Model description

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Intended uses & limitations

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Training Procedure

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Hyperparameters

<details> <summary> Click to expand </summary>

Hyperparameter	Value
memory
steps	[('columntransformer', ColumnTransformer(transformers=[('pipeline-1',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer()),<br /> ('standardscaler',<br /> StandardScaler())]),<br /> ['Course_Avg_Roll_1y', 'Course_Min_Last_3y',<br /> 'Course_Max_Last_3y', 'Course_Std_Last_3y']),<br /> ('pipeline-2',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_b...<br /> SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))]),<br /> ['CourseLevel', 'Years_Since_Start',<br /> 'Prof_Courses_Taught', 'Year']),<br /> ('drop', 'drop',<br /> ['Reported', 'Section', 'Detail', 'Median',<br /> 'Percentile (25)', 'Percentile (75)', 'High',<br /> 'Low', '<50', '50-54', '55-59', '60-63',<br /> '64-67', '68-71', '72-75', '76-79', '80-84',<br /> '85-89', '90-100'])])), ('ridge', Ridge(alpha=2.091, random_state=42))]
transform_input
verbose	False
columntransformer	ColumnTransformer(transformers=[('pipeline-1',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer()),<br /> ('standardscaler',<br /> StandardScaler())]),<br /> ['Course_Avg_Roll_1y', 'Course_Min_Last_3y',<br /> 'Course_Max_Last_3y', 'Course_Std_Last_3y']),<br /> ('pipeline-2',<br /> Pipeline(steps=[('simpleimputer',<br /> SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_b...<br /> SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))]),<br /> ['CourseLevel', 'Years_Since_Start',<br /> 'Prof_Courses_Taught', 'Year']),<br /> ('drop', 'drop',<br /> ['Reported', 'Section', 'Detail', 'Median',<br /> 'Percentile (25)', 'Percentile (75)', 'High',<br /> 'Low', '<50', '50-54', '55-59', '60-63',<br /> '64-67', '68-71', '72-75', '76-79', '80-84',<br /> '85-89', '90-100'])])
ridge	Ridge(alpha=2.091, random_state=42)
columntransformer__force_int_remainder_cols	True
columntransformer__n_jobs
columntransformer__remainder	drop
columntransformer__sparse_threshold	0.3
columntransformer__transformer_weights
columntransformer__transformers	[('pipeline-1', Pipeline(steps=[('simpleimputer', SimpleImputer()),<br /> ('standardscaler', StandardScaler())]), ['Course_Avg_Roll_1y', 'Course_Min_Last_3y', 'Course_Max_Last_3y', 'Course_Std_Last_3y']), ('pipeline-2', Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_binary', handle_unknown='ignore'))]), ['Campus', 'Session', 'SubjectCourse', 'Professor', 'Subject']), ('pipeline-3', Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))]), ['CourseLevel', 'Years_Since_Start', 'Prof_Courses_Taught', 'Year']), ('drop', 'drop', ['Reported', 'Section', 'Detail', 'Median', 'Percentile (25)', 'Percentile (75)', 'High', 'Low', '<50', '50-54', '55-59', '60-63', '64-67', '68-71', '72-75', '76-79', '80-84', '85-89', '90-100'])]
columntransformer__verbose	False
columntransformer__verbose_feature_names_out	True
columntransformer__pipeline-1	Pipeline(steps=[('simpleimputer', SimpleImputer()),<br /> ('standardscaler', StandardScaler())])
columntransformer__pipeline-2	Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('onehotencoder',<br /> OneHotEncoder(drop='if_binary', handle_unknown='ignore'))])
columntransformer__pipeline-3	Pipeline(steps=[('simpleimputer', SimpleImputer(strategy='most_frequent')),<br /> ('ordinalencoder',<br /> OrdinalEncoder(handle_unknown='use_encoded_value',<br /> unknown_value=-1))])
columntransformer__drop	drop
columntransformer__pipeline-1__memory
columntransformer__pipeline-1__steps	[('simpleimputer', SimpleImputer()), ('standardscaler', StandardScaler())]
columntransformer__pipeline-1__transform_input
columntransformer__pipeline-1__verbose	False
columntransformer__pipeline-1__simpleimputer	SimpleImputer()
columntransformer__pipeline-1__standardscaler	StandardScaler()
columntransformer__pipeline-1__simpleimputer__add_indicator	False
columntransformer__pipeline-1__simpleimputer__copy	True
columntransformer__pipeline-1__simpleimputer__fill_value
columntransformer__pipeline-1__simpleimputer__keep_empty_features	False
columntransformer__pipeline-1__simpleimputer__missing_values	nan
columntransformer__pipeline-1__simpleimputer__strategy	mean
columntransformer__pipeline-1__standardscaler__copy	True
columntransformer__pipeline-1__standardscaler__with_mean	True
columntransformer__pipeline-1__standardscaler__with_std	True
columntransformer__pipeline-2__memory
columntransformer__pipeline-2__steps	[('simpleimputer', SimpleImputer(strategy='most_frequent')), ('onehotencoder', OneHotEncoder(drop='if_binary', handle_unknown='ignore'))]
columntransformer__pipeline-2__transform_input
columntransformer__pipeline-2__verbose	False
columntransformer__pipeline-2__simpleimputer	SimpleImputer(strategy='most_frequent')
columntransformer__pipeline-2__onehotencoder	OneHotEncoder(drop='if_binary', handle_unknown='ignore')
columntransformer__pipeline-2__simpleimputer__add_indicator	False
columntransformer__pipeline-2__simpleimputer__copy	True
columntransformer__pipeline-2__simpleimputer__fill_value
columntransformer__pipeline-2__simpleimputer__keep_empty_features	False
columntransformer__pipeline-2__simpleimputer__missing_values	nan
columntransformer__pipeline-2__simpleimputer__strategy	most_frequent
columntransformer__pipeline-2__onehotencoder__categories	auto
columntransformer__pipeline-2__onehotencoder__drop	if_binary
columntransformer__pipeline-2__onehotencoder__dtype	<class 'numpy.float64'>
columntransformer__pipeline-2__onehotencoder__feature_name_combiner	concat
columntransformer__pipeline-2__onehotencoder__handle_unknown	ignore
columntransformer__pipeline-2__onehotencoder__max_categories
columntransformer__pipeline-2__onehotencoder__min_frequency
columntransformer__pipeline-2__onehotencoder__sparse_output	True
columntransformer__pipeline-3__memory
columntransformer__pipeline-3__steps	[('simpleimputer', SimpleImputer(strategy='most_frequent')), ('ordinalencoder', OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1))]
columntransformer__pipeline-3__transform_input
columntransformer__pipeline-3__verbose	False
columntransformer__pipeline-3__simpleimputer	SimpleImputer(strategy='most_frequent')
columntransformer__pipeline-3__ordinalencoder	OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1)
columntransformer__pipeline-3__simpleimputer__add_indicator	False
columntransformer__pipeline-3__simpleimputer__copy	True
columntransformer__pipeline-3__simpleimputer__fill_value
columntransformer__pipeline-3__simpleimputer__keep_empty_features	False
columntransformer__pipeline-3__simpleimputer__missing_values	nan
columntransformer__pipeline-3__simpleimputer__strategy	most_frequent
columntransformer__pipeline-3__ordinalencoder__categories	auto
columntransformer__pipeline-3__ordinalencoder__dtype	<class 'numpy.float64'>
columntransformer__pipeline-3__ordinalencoder__encoded_missing_value	nan
columntransformer__pipeline-3__ordinalencoder__handle_unknown	use_encoded_value
columntransformer__pipeline-3__ordinalencoder__max_categories
columntransformer__pipeline-3__ordinalencoder__min_frequency
columntransformer__pipeline-3__ordinalencoder__unknown_value	-1
ridge__alpha	2.091
ridge__copy_X	True
ridge__fit_intercept	True
ridge__max_iter
ridge__positive	False
ridge__random_state	42
ridge__solver	auto
ridge__tol	0.0001

</details>

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Task: Tabular Regression
Library: sklearn
Created: Dec 20, 2024
Updated: Dec 20, 2024