Sci-kit learn function to select threshold for higher recall than precision
As far as a fuction in scikit to implement a certain threshold for a higher recall, I don't think there is one.

But, depending on what model you're using, you can vary the threshold for probability outputs of the model to obtain a higher Recall.

For example, say you are using a Random Forest or Logistic Regression classifier model, then you can use the predict_proba function on your data set; this will return probabilities for each sample belonging to a particular class. If you then create a new array from the output of predict_proba using your threshold, then the model will have a Recall with your desired accuracy when it classifies new unseen data.

e.g pseudo code for binary classification

clf = sklearn.ensemble.RandomForestClassifier()
model = fit(X,y) # fit model to training datset
probs = model.predict_proba(X_new) # prediction on a new dataset X_new

threshold = 0.7 # threshold we set where the probability prediction must be above this to be classified as a '1'
classes = probs[:,1] # say it is the class in the second column you care about predictint
classes[classes&gt;=threshold] = 1
classes[classes&lt;threshold] = 0