加载虹膜数据集并将其分成训练集和测试集。在训练集上训练具有虹膜所有属性的决策树模型。然后使用训练好的模型与测试集进行预测。
时间: 2023-06-14 20:03:48 浏览: 61
好的,我来为您提供一下Python代码实现。
首先,我们需要加载虹膜数据集。可以使用sklearn库中的load_iris函数来加载数据集。
```python
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
```
接下来,我们需要将数据集分成训练集和测试集。可以使用sklearn库中的train_test_split函数来进行分割。
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
```
然后,我们可以使用sklearn库中的DecisionTreeClassifier类来训练决策树模型。在训练之前,我们需要先实例化一个DecisionTreeClassifier对象,并将其传递给fit函数进行训练。
```python
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
```
训练完成后,我们可以使用模型对测试集进行预测,并使用sklearn库中的accuracy_score函数来计算准确率。
```python
from sklearn.metrics import accuracy_score
y_pred = dtc.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)
```
完整代码如下:
```python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
# Load iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split dataset into training and testing set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Train decision tree model
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
# Predict on test set and calculate accuracy
y_pred = dtc.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)
```