I am doing a binary classification. May I know how to extract the real indexes of the misclassified or classified instances of the training data frame while doing K fold cross-validation? I found no answer to this question here.
I got the values in folds as described here:
skf=StratifiedKFold(n_splits=10,random_state=111,shuffle=False)
cv_results = cross_val_score(model, X_train, y_train, cv=skf, scoring='roc_auc')
fold_pred = [pred[j] for i, j in skf.split(X_train,y_train)]
fold_pred
Is there any method to get index of misclassified (or classified ones)? So the output is a dataframe that only has misclassified(or classified) instances while doing cross validation.
Desired output: Missclassified instances in the dataframe with real indices.
col1 col2 col3 col4 target
13 0 1 0 0 0
14 0 1 0 0 0
18 0 1 0 0 1
22 0 1 0 0 0
where input has 100 instances, 4 are misclassified (index number 13,14,18 and 22) while doing CV
From cross_val_predict you already have the predictions. It's a matter of subsetting your data frame where the predictions are not the same as your true label, for example:
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_predict, StratifiedKFold
from sklearn.datasets import load_breast_cancer
import pandas as pd
data = load_breast_cancer()
df = pd.DataFrame(data.data[:,:5],columns=data.feature_names[:5])
df['label'] = data.target
rfc = RandomForestClassifier()
skf = StratifiedKFold(n_splits=10,random_state=111,shuffle=True)
pred = cross_val_predict(rfc, df.iloc[:,:5], df['label'], cv=skf)
df[df['label']!=pred]
mean radius mean texture ... mean smoothness label
3 11.42 20.38 ... 0.14250 0
5 12.45 15.70 ... 0.12780 0
9 12.46 24.04 ... 0.11860 0
22 15.34 14.26 ... 0.10730 0
31 11.84 18.70 ... 0.11090 0