---
library_name: sklearn
tags:
- sklearn
- skops
- tabular-classification
model_file: model.pkl
widget:
structuredData:
angel_n_rounds:
- 0.0
- 0.0
- 0.0
pre_seed_n_rounds:
- 0.0
- 0.0
- 0.0
seed_funding:
- 1250000.0
- 800000.0
- 8000000.0
seed_n_rounds:
- 1.0
- 3.0
- 1.0
time_first_funding:
- 1270.0
- 1856.0
- 689.0
time_till_series_a:
- 1455.0
- 1667.0
- 1559.0
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
Click to expand
| 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 |
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))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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))])
ColumnTransformer(transformers=[('min_max_scaler', MinMaxScaler(),['time_first_funding', 'seed_funding','time_till_series_a'])])
['time_first_funding', 'seed_funding', 'time_till_series_a']
MinMaxScaler()
LogisticRegression(penalty='none', random_state=0)