semvec/pypi-packages huggingface dataset:
WeightedEnsemble 1.0.0
will help in getting the correct classification model
Requires Python: >=3.6

Classification.Weighted_Ensemble(Models,X_train,y_train,X_val,y_val,X_test,y_test,Method='SLSQP')

Any classification model  or an average Ensembling model combines the prediction from each model equally and often results in better performance on average than a given single model.

Weighted Ensemble model is an approach that allows multiple models to contribute to a prediction in proportion to their trust or estimated performance.

Using this above mentoined Weighted_Ensemble class You can predict single model as well as multiple models Which will give  the clarity about the weightage of the model with respect to inputs 

Parameters:
Models==List of the model objects that you wanted to Predict 
X_train==Traininig dataset
y_train==Training label dataset 
x_val==Validation data set
Y_val==Validation labels dataset
#X_val and y_Val are used to calculate the weights of the model objects that are there in the Models List 
NOte:The sum of the weights of the modes mentioned in the object are always equal to 1
x_test==Testing dataset to calculate the metric score
Y_test==Testing label dataset to calculate the metric score

Example 
in the code  declare models=[]
Model1:
rf=Randomclassifier()
models.append(rf)

Model2:
xgb=XGBClassifier()
models.append(xgb)

result,weights=Weighted_Ensemble(Models,X_train,y_train,X_val,y_val,X_test,y_test,Method='SLSQP')

Result is a data frame which is something like below
threshold	tp	fp	tn	fn	accuracy	precision	recall	scope	f1	fpr
Threshold values range from o.1 to 0.99 
Weights  are the list of values that shows how much weightage it has given to the model 
We can compare the values and pick the model very easily 

Classification.get_metric_score(y_true, y_proba):
If we have model already in hand we can call this method to observe the  perfomance of the model  with various thershold values 
Result is a data frame which is something like below
threshold	tp	fp	tn	fn	accuracy	precision	recall	scope	f1	fpr
Threshold values range from o.1 to 0.99  

Parameters:
y_true==actual y values
y_proba==We should give the probability values with respect to the predcition once

Example:
y_true is the actual values
Y_proba=np.round(model.predict_proba(X_test)[:,1],3)
result_df=get_metric_score(y_true, y_proba)
cxsq 0.2.0
Auto submitter for xg.hit.edu.cn xsCxsq
Requires Python: >=3.5, <4
Keywords: Harbin Institute of Technology,HIT,requests

# 学工平台出校申请

本项目为使用命令行提交哈尔滨工业大学[学工平台](https://xg.hit.edu.cn/)[出入校申请](https://xg.hit.edu.cn/zhxy-xgzs/xg_mobile/xsCxsq)的工具。实现了按照配置文件登录，申请出校的功能。

## 如何使用

1. `pip install cxsq`
2. 按照下面模板填写账号密码等信息
3. 运行 `cxsq -c config_file [-d date1 date2 ... | -T | -t]`

## 出校日期参数

参数可多选，添加多个日期

- `-d date ...` 申请指定日期的临时出校，`date` 格式为 `yyyyMMdd`
- `-t` 申请今日出校
- `-T` 申请明日出校

## 配置文件模板

```yaml
# 统一身份认证账号
username: "1200000000"
# 统一身份认证密码
password: "PASSWORD"
# 出校理由, 可选, 默认为空格
reasons: ["吃饭", "睡觉", "打豆豆"]
```

## 开源许可证

[AGPL-3.0](./LICENSE)

简单来说，建议您做到以下几点：

- 任何基于或与本项目有间接接触的项目均使用 AGPL-3.0 协议
- 当你使用本项目或对其修改时，如果你所服务的对象向您索要源代码，请不要拒绝
- 请不要将本项目用于商业用途

## 免责声明

本项目仅为方便通过命令行进行每日上报的工具。按照 [LICENSE](./LICENSE)，开发者不对本工具的使用负责。