CODEWITHSUNDEEP
pythonscikit-learn
joey11235
joey11235

Reputation: 53

How to get the predicted probabilities of a classification model?

I'm trying out different classification models using a binary dependent variable (occupied/unoccupied). The models I am interested in are Logistic regression, Decision tree and Gaussian Naïve Bayes.

My input data is a csv-file with a datetime index (e.g. 2019-01-07 14:00), three variable columns ("R", "P", "C", containing numerical values), and the dependent variable column ("value", containing the binary values).

Training the model is not the problem, that all works fine. All the models give me their prediction in binary values (this of course should be the ultimate outcome), but I would also like to see the predicted probabilities which made them decide on either of the binary values. Is there any way to get also these values?

I have tried all of the classification visualizers that function with the yellowbrick package (ClassBalance, ROCAUC, ClassificationReport, ClassPredictionError). But all of these don't give me a graph that shows the calculated probabilities by the model for the data set. 

import pandas as pd
import numpy as np
data = pd.read_csv('testrooms_data.csv', parse_dates=['timestamp'])


from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

##split dataset into test and trainig set
X = data.drop("value", axis=1) # X contains all the features
y = data["value"] # y contains only the label

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 1)

###model training
###Logistic Regression###
clf_lr = LogisticRegression()

# fit the dataset into LogisticRegression Classifier

clf_lr.fit(X_train, y_train)
#predict on the unseen data
pred_lr = clf_lr.predict(X_test)

###Decision Tree###

from sklearn.tree import DecisionTreeClassifier

clf_dt = DecisionTreeClassifier()
pred_dt = clf_dt.fit(X_train, y_train).predict(X_test)

###Bayes###
from sklearn.naive_bayes import GaussianNB

bayes = GaussianNB()
pred_bayes = bayes.fit(X_train, y_train).predict(X_test)


###visualization for e.g. LogReg
from yellowbrick.classifier import ClassificationReport
from yellowbrick.classifier import ClassPredictionError
from yellowbrick.classifier import ROCAUC

#classificationreport
visualizer = ClassificationReport(clf_lr, support=True)

visualizer.fit(X_train, y_train)  # Fit the visualizer and the model
visualizer.score(X_test, y_test)  # Evaluate the model on the test data
g = visualizer.poof()             # Draw/show/poof the data

#classprediction report
visualizer2 = ClassPredictionError(LogisticRegression())

visualizer2.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer2.score(X_test, y_test) # Evaluate the model on the test data
g2 = visualizer2.poof() # Draw visualization

#(ROC)
visualizer3 = ROCAUC(LogisticRegression())

visualizer3.fit(X_train, y_train)  # Fit the training data to the visualizer
visualizer3.score(X_test, y_test)  # Evaluate the model on the test data
g3 = visualizer3.poof()             # Draw/show/poof the data

it would be great to have e.g. an array similar to pred_lr that contains the probabilities calculated for each row of the csv file. Is that possible? If yes, how can I get it?

Upvotes: 3

Views: 6023

Answers (1)

BCJuan
BCJuan

Reputation: 825

In most sklearn estimators (if not all) you have a method for obtaining the probability that precluded the classification, either in log probability or probability.

For example, if you have your Naive Bayes classifier and you want to obtain probabilities but not classification itself, you could do (I used same nomenclatures as in your code):

from sklearn.naive_bayes import GaussianNB

bayes = GaussianNB()
pred_bayes = bayes.fit(X_train, y_train).predict(X_test)

#for probabilities
bayes.predict_proba(X_test)
bayes.predict_log_proba(X_test)

Hope this helps.

Upvotes: 4

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