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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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Model Card Authors

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model_card_authors

jirko

model_description

just the temporal regression with reduced input features

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