Model description

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Intended uses & limitations

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Training Procedure

Hyperparameters

The model is trained with below hyperparameters.

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Hyperparameter	Value
memory
steps	[('transformation', ColumnTransformer(transformers=[('min_max_scaler', MinMaxScaler(), ['time_first_funding', 'seed_funding', 'time_till_series_a'])])), ('model', LogisticRegression(penalty='none', random_state=0))]
verbose	False
transformation	ColumnTransformer(transformers=[('min_max_scaler', MinMaxScaler(), ['time_first_funding', 'seed_funding', 'time_till_series_a'])])
model	LogisticRegression(penalty='none', random_state=0)
transformation__n_jobs
transformation__remainder	drop
transformation__sparse_threshold	0.3
transformation__transformer_weights
transformation__transformers	[('min_max_scaler', MinMaxScaler(), ['time_first_funding', 'seed_funding', 'time_till_series_a'])]
transformation__verbose	False
transformation__verbose_feature_names_out	True
transformation__min_max_scaler	MinMaxScaler()
transformation__min_max_scaler__clip	False
transformation__min_max_scaler__copy	True
transformation__min_max_scaler__feature_range	(0, 1)
model__C	1.0
model__class_weight
model__dual	False
model__fit_intercept	True
model__intercept_scaling	1
model__l1_ratio
model__max_iter	100
model__multi_class	auto
model__n_jobs
model__penalty	none
model__random_state	0
model__solver	lbfgs
model__tol	0.0001
model__verbose	0
model__warm_start	False

Model Plot

The model plot is below.

Pipeline(steps=[('transformation',ColumnTransformer(transformers=[('min_max_scaler',MinMaxScaler(),['time_first_funding','seed_funding','time_till_series_a'])])),('model', LogisticRegression(penalty='none', random_state=0))])

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Evaluation Results

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How to Get Started with the Model

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Model Card Authors

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Citation

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model_card_authors

jirko

model_description

just the temporal regression with reduced input features