导入数据用deep fuzzy forest预测三分类数据python
时间: 2024-02-11 13:05:55 浏览: 20
以下是使用Deep Fuzzy Forest(DFF)预测三分类数据的Python代码:
首先,需要导入必要的库和函数:
```python
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from deepforest import CascadeForestClassifier
```
接下来,生成一个三分类的样本数据:
```python
X, y = make_classification(n_samples=1000, n_classes=3, n_features=10, random_state=42)
```
然后,将数据集划分为训练集和测试集:
```python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
接下来,定义DFF分类器并对其进行训练:
```python
clf = CascadeForestClassifier(n_estimators=100, n_trees=10)
clf.fit(X_train, y_train)
```
最后,使用训练好的模型对测试集进行预测并评估模型性能:
```python
y_pred = clf.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
这就是使用Deep Fuzzy Forest预测三分类数据的Python代码。