kaggle共享单车案例的可加模型分析与python代码
时间: 2023-11-13 13:31:09 浏览: 37
这里是一个Kaggle共享单车案例的模型分析和Python代码,主要使用了可加模型(Additive Model)进行预测。
首先,我们需要导入必要的库和数据集:
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
```
接下来,我们进行数据预处理。首先,我们将时间特征进行拆分,并且删除一些无用的特征:
```python
train['datetime'] = pd.to_datetime(train['datetime'])
train['year'] = train['datetime'].dt.year
train['month'] = train['datetime'].dt.month
train['day'] = train['datetime'].dt.day
train['hour'] = train['datetime'].dt.hour
train['minute'] = train['datetime'].dt.minute
train['second'] = train['datetime'].dt.second
test['datetime'] = pd.to_datetime(test['datetime'])
test['year'] = test['datetime'].dt.year
test['month'] = test['datetime'].dt.month
test['day'] = test['datetime'].dt.day
test['hour'] = test['datetime'].dt.hour
test['minute'] = test['datetime'].dt.minute
test['second'] = test['datetime'].dt.second
train.drop(['datetime', 'casual', 'registered'], axis=1, inplace=True)
test.drop(['datetime'], axis=1, inplace=True)
```
然后,我们将数据集分成训练集和验证集:
```python
from sklearn.model_selection import train_test_split
X = train.drop(['count'], axis=1)
y = train['count']
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
```
接下来,我们使用可加模型进行预测。我们首先定义一个可加模型类:
```python
class AdditiveModel:
def __init__(self, models):
self.models = models
def fit(self, X, y):
for model in self.models:
model.fit(X, y)
def predict(self, X):
predictions = np.column_stack([
model.predict(X) for model in self.models
])
return np.sum(predictions, axis=1)
```
然后,我们定义一些基本的线性模型和树模型,并将它们放入可加模型中:
```python
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import RandomForestRegressor
linear_models = [LinearRegression(), Ridge(alpha=0.1)]
tree_models = [RandomForestRegressor(n_estimators=100, random_state=42)]
linear_additive = AdditiveModel(linear_models)
tree_additive = AdditiveModel(tree_models)
```
我们使用训练集拟合可加模型并进行预测:
```python
linear_additive.fit(X_train, y_train)
tree_additive.fit(X_train, y_train)
linear_additive_preds = linear_additive.predict(X_val)
tree_additive_preds = tree_additive.predict(X_val)
```
最后,我们使用均方根误差(RMSE)评估模型的性能:
```python
from sklearn.metrics import mean_squared_error
linear_additive_rmse = np.sqrt(mean_squared_error(y_val, linear_additive_preds))
tree_additive_rmse = np.sqrt(mean_squared_error(y_val, tree_additive_preds))
print("Linear additive RMSE:", linear_additive_rmse)
print("Tree additive RMSE:", tree_additive_rmse)
```
这里我们使用了两个基本的线性模型和一个随机森林模型进行预测,并将它们放入可加模型中。最后,我们使用均方根误差评估了这两个可加模型的性能。