metadata
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
model_format: skops
model_file: model.skops
widget:
structuredData:
x0:
- -0.8513550738681201
- 0.3565756375241982
- -0.5493723960200406
x1:
- -0.9801306786815437
- 0.16144422497410207
- -0.5044744688250247
x2:
- -0.40478372420423153
- 0.465368421656243
- -0.6223217606693501
x3:
- -0.5539725609683268
- 0.3927870023121129
- 1.2133119571551605
x4:
- -0.3313192794050237
- -0.5263980861381337
- 0.14244353694681483
x5:
- -0.6076784605515674
- -0.3021390244014409
- 0.37259389709675395
x6:
- 0.31079384041548314
- -0.11643850592424994
- -0.7648620670356181
x7:
- -0.7921692833892792
- 0.5610338186827566
- -0.707594089509777
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 |
---|---|
C | 1.0 |
class_weight | |
dual | False |
fit_intercept | True |
intercept_scaling | 1 |
l1_ratio | |
max_iter | 100 |
multi_class | auto |
n_jobs | |
penalty | l2 |
random_state | 0 |
solver | lbfgs |
tol | 0.0001 |
verbose | 0 |
warm_start | False |
Model Plot
The model plot is below.
LogisticRegression(random_state=0)Please rerun this cell to show the HTML repr or trust the notebook.
LogisticRegression(random_state=0)
Evaluation Results
You can find the details about evaluation process and the evaluation results.
Metric | Value |
---|---|
Train Accuracy | 0.791531 |
Test Accuracy | 0.714286 |
How to Get Started with the Model
[More Information Needed]
Model Card Authors
This model card is written by following authors:
[More Information Needed]
Model Card Contact
You can contact the model card authors through following channels: [More Information Needed]
Citation
Below you can find information related to citation.
BibTeX:
[More Information Needed]
limitations
Mô hình chưa thể dùng trong production.
model_description
Regression model thử nghiệm với skops.