exp(confint(Muti_uni)) :glm.fit: fitted probabilities numerically 0 or 1 occurred
时间: 2024-05-24 10:11:20 浏览: 17
This error message is indicating that the logistic regression model is having difficulty fitting the data, resulting in probabilities that are either 0 or 1. This can occur when the model is overfitting the data, which means that it is too complex and is fitting the noise in the data rather than the underlying patterns.
One solution is to simplify the model by reducing the number of predictor variables, or by using regularization techniques such as ridge or lasso regression. It may also be helpful to examine the data and ensure that there are no outliers or other issues that could be causing the model to struggle.
相关问题
AttributeError: type object 'SolutionOutputs' has no attribute 'muti_hand_landmarks'
这个错误通常发生在你尝试使用一个不存在的属性或方法时。根据错误信息,它指出在类型为`SolutionOutputs`的对象上找不到`muti_hand_landmarks`属性。
可能的原因是你拼写错误或者该属性不存在于该对象中。请检查你的代码,确保正确拼写了属性名,并且确保该属性在该对象中存在。如果问题仍然存在,请提供更多的上下文和代码,以便我能够更好地帮助你解决问题。
forest = RandomForestClassifier(max_depth=6, min_samples_leaf=10, n_estimators=1000) def muti_score(model): # warnings.filterwarnings('ignore') accuracy = cross_val_score(model, X_train, Y_train, scoring='accuracy', cv=5) precision = cross_val_score(model, X_train, Y_train, scoring='precision', cv=5) recall = cross_val_score(model, X_train, Y_train, scoring='recall', cv=5) f1_score = cross_val_score(model, X_train, Y_train, scoring='f1', cv=5) auc = cross_val_score(model, X_train, Y_train, scoring='roc_auc', cv=5) print("准确率:",accuracy.mean()) print("精确率:",precision.mean()) print("召回率:",recall.mean()) print("F1_score:",f1_score.mean()) print("AUC:",auc.mean()) muti_score(forest)
这是一段Python代码,定义了一个名为forest的随机森林分类器模型,其中max_depth表示树的最大深度为6,min_samples_leaf表示最小叶子节点样本数为10,n_estimators表示模型中树的数量为1000。
另外,代码还定义了一个名为muti_score的函数,该函数的参数为一个模型,用于计算模型的多个评价指标。
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