| import pandas as pd | |
| import streamlit as st | |
| from sklearn.datasets import make_classification | |
| from sklearn.model_selection import cross_val_score | |
| from sklearn.metrics import average_precision_score | |
| from sklearn.linear_model import RidgeClassifier | |
| import lightgbm | |
| X, y = make_classification(n_samples=10_000 | |
| , n_features=7 | |
| , n_redundant=3 | |
| , weights=[0.99] | |
| ) | |
| st.title('python') | |
| st.table(pd.DataFrame(y).value_counts()) | |
| print(pd.DataFrame(y).value_counts()) |