y_pred += tree.predict_proba(fuzzy_vals)是什么意思
时间: 2023-06-10 17:08:25 浏览: 46
`tree` 是一个决策树模型,`fuzzy_vals` 是一个包含一些特征值的数组。`predict_proba()` 是一个方法,用于预测 `fuzzy_vals` 在每个可能的类别(对于分类问题)或每个可能的输出值(对于回归问题)上的概率分布。因此,`y_pred` 将是一个数组,其中每个元素都表示 `fuzzy_vals` 属于相应类别或输出值的概率。在分类问题中,`y_pred` 的每个元素表示 `fuzzy_vals` 属于相应类别的概率,而在二元分类问题中,`y_pred` 只有两个元素。
相关问题
y_pred = np.zeros((y_train.shape[0], 3)) for tree in forest: a = [] for j in range(X_train.shape[1]): if np.median(X_train[:, j]) > np.mean(X_train[:, j]): fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])]) a.append(fuzzy_vals) fuzzy_vals = np.array(a).T y_pred += tree.predict_proba(fuzzy_vals) y_pred /= n_trees改成三分类预测代码
如果你的随机森林模型是二分类的,你需要做以下更改来进行三分类预测:
1. 修改 `y_pred` 的形状为 `(y_train.shape[0], 3)`,即有三个类别。
2. 对于每个决策树,预测的输出应该是每个类别的概率,而不是二分类的概率。所以你需要将 `tree.predict_proba(fuzzy_vals)` 改为 `tree.predict_proba(fuzzy_vals.reshape(-1, fuzzy_vals.shape[1], 1)).reshape(-1, 3)`。
3. 对于每个样本的预测值,你需要将它除以 `n_trees`,而不是 `n_trees` 乘以它。
以下是修改后的代码:
```
y_pred = np.zeros((y_train.shape[0], 3))
for tree in forest:
a = []
for j in range(X_train.shape[1]):
if np.median(X_train[:, j]) > np.mean(X_train[:, j]):
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])])
else:
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])])
a.append(fuzzy_vals)
fuzzy_vals = np.array(a).T
y_pred += tree.predict_proba(fuzzy_vals.reshape(-1, fuzzy_vals.shape[1], 1)).reshape(-1, 3)
y_pred /= n_trees
```
y_pred = np.zeros(y_train.shape) for tree in forest: a = [] for j in range(X_train.shape[1]): if np.median(X_train[:, j]) > np.mean(X_train[:, j]): fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])]) else: fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])]) a.append(fuzzy_vals) fuzzy_vals = np.array(a).T y_pred += tree.predict_proba(fuzzy_vals)[:, 1] y_pred /= n_trees怎么改成分类数据的输出
如果要将这段代码修改为分类数据的输出,需要修改两个地方:
1. 修改y_pred的初始化:分类数据的输出应该是离散的类别标签,而不是概率值。因此,我们需要将y_pred初始化为一个长度为样本数的数组,每个元素的取值为该样本所属的类别标签。
2. 修改y_pred的计算方式:在每棵决策树中,我们需要将输出的概率转换为类别标签,然后将所有决策树的输出取平均值作为最终的预测结果。
下面是修改后的代码:
```python
y_pred = np.zeros(y_train.shape[0])
for tree in forest:
a = []
for j in range(X_train.shape[1]):
if np.median(X_train[:, j]) > np.mean(X_train[:, j]):
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.mean(X_train[:, j]), np.median(X_train[:, j]), np.max(X_train[:, j])])
else:
fuzzy_vals = fuzz.trapmf(X_train[:, j], [np.min(X_train[:, j]), np.median(X_train[:, j]), np.mean(X_train[:, j]), np.max(X_train[:, j])])
a.append(fuzzy_vals)
fuzzy_vals = np.array(a).T
y_proba = tree.predict_proba(fuzzy_vals)
# 将概率转换为类别标签
y_tree = np.argmax(y_proba, axis=1)
y_pred += y_tree
# 将所有决策树的输出取平均值作为最终的预测结果
y_pred /= n_trees
```
注意,这里假设分类数据的类别标签是从0开始的整数。如果标签不是这种形式,需要根据实际情况进行调整。