Python RandomForest - 未知标签错误
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Python RandomForest - Unknown label Error
提问by Dragonfly
I have trouble using RandomForest fit function
我在使用 RandomForest 拟合函数时遇到问题
This is my training set
这是我的训练集
P1 Tp1 IrrPOA Gz Drz2
0 0.0 7.7 0.0 -1.4 -0.3
1 0.0 7.7 0.0 -1.4 -0.3
2 ... ... ... ... ...
3 49.4 7.5 0.0 -1.4 -0.3
4 47.4 7.5 0.0 -1.4 -0.3
... (10k rows)
I want to predict P1 thanks to all the other variables using sklearn.ensemble RandomForest
由于使用 sklearn.ensemble RandomForest 的所有其他变量，我想预测 P1
colsRes = ['P1']
X_train = train.drop(colsRes, axis = 1)
Y_train = pd.DataFrame(train[colsRes])
rf = RandomForestClassifier(n_estimators=100)
rf.fit(X_train, Y_train)
Here is the error I get:
这是我得到的错误：
ValueError: Unknown label type: array([[ 0. ],
[ 0. ],
[ 0. ],
...,
[ 49.4],
[ 47.4],
I did not find anything about this label error, I use Python 3.5. Any advice would be a great help !
我没有发现有关此标签错误的任何信息，我使用的是 Python 3.5。任何建议都会有很大帮助！
采纳答案by Gurupad Hegde
When you are passing label (y) data to rf.fit(X,y), it expects y to be 1D list. Slicing the Panda frame always result in a 2D list. So, conflict raised in your use-case. You need to convert the 2D list provided by pandas DataFrame to a 1D list as expected by fit function.
当您将标签 (y) 数据传递给 时 rf.fit(X,y)，它期望 y 是一维列表。对 Panda 框架进行切片总是会产生一个 2D 列表。因此，在您的用例中引发了冲突。您需要将 pandas DataFrame 提供的二维列表转换为 fit 函数预期的一维列表。
Try using 1D list first:
首先尝试使用一维列表：
Y_train = list(train.P1.values)
If this does not solve the problem, you can try with solution mentioned in MultinomialNB error: "Unknown Label Type":
如果这不能解决问题，您可以尝试使用MultinomialNB error: "Unknown Label Type" 中提到的解决方案：
Y_train = np.asarray(train['P1'], dtype="|S6")
So your code becomes,
所以你的代码变成，
colsRes = ['P1']
X_train = train.drop(colsRes, axis = 1)
Y_train = np.asarray(train['P1'], dtype="|S6")
rf = RandomForestClassifier(n_estimators=100)
rf.fit(X_train, Y_train)
回答by N. Wouda
According to this SO post, Classifiers need integer or string labels.
根据this SO post，分类器需要整数或字符串标签。
You could consider switching to a regression model instead (that mightbetter suit your data, as each datum appears to be a float), like so:
您可以考虑改用回归模型（这可能更适合您的数据，因为每个数据似乎都是一个浮点数），如下所示：
X_train = train.drop('P1', axis=1)
Y_train = train['P1']
rf = RandomForestRegressor(n_estimators=100)
rf.fit(X_train.as_matrix(), Y_train.as_matrix())
回答by RunD.M.C.
may be a tad late to the party but I just got this error and solved it by making sure my y variable was type(int) using
聚会可能有点晚了，但我刚刚收到此错误并通过确保我的 y 变量是 type(int) 来解决它
y = df['y_variable'].astype(int)
before doing a train test split, also like others have said you problem seems better fit with a RFReg rather then RF
在进行火车测试拆分之前，也像其他人所说的那样，您的问题似乎更适合 RFReg 而不是 RF
