Questions tagged [probability-calibration]

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2
votes
1answer
55 views

How can i tell if my model is overfitting from the distribution of predicted probabilities?

all, i am training light gradient boosting and have used all of the necessary parameters to help in over fitting.i plot the predicted probabilities (i..e probabililty has cancer) distribution from the ...
4
votes
1answer
267 views

XGBoost: how to adjust the probabilities of a binary classifier to match training data?

Training and testing data have around 1% positives, but the model predicts only around 0.1% as positives. The model is an xgboost classifier. I’ve tried calibration but it didn’t improve much. I ...
1
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0answers
36 views

xgboost calibration kde plots (isotonic) not smooth

i am training my xgboost model on an imbalanced binary classification problem. It is important to me to have well calibrated probabilities so i have chosen to optimize the brier score. I then plot the ...
1
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2answers
40 views

Determining threshold in an area with very few samples of positive label

I have a binary classification task where I want to either keep or discard samples. I have about a million samples, and about 1% should be kept. I want to discard as much as possible, but discarding ...
2
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1answer
35 views
1
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0answers
29 views

calibrating classifier probabilities for unbalanced data when class ratios are unknown

I've built a binary classification convolutional neutral network, trained on simulated data with equal numbers of simulations for each class. I've obtained good results for a validation set with equal ...
0
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0answers
16 views

using logistic:binary as objective in xgboost

i am using 'binary:logisitic' as my objective function in xgboost classifier. Therefore when i predict probabilities i.e: model.predict_proba(x_test)[:, 1] will ...
2
votes
1answer
26 views

How to determine the correct target for classification probability when the observed samples are probabilities of each class?

I have data in which each event's outcome can be described by a probability of a categorical occurrence. For example, if all of the possible class outcomes are A, B, C, or D suppose in one event 7/10 ...
18
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4answers
1k views

XGBoost outputs tend towards the extremes

I am currently using XGBoost for risk prediction, it seems to be doing a good job in the binary classification department but the probability outputs are way off, i.e., changing the value of a feature ...
0
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0answers
28 views

Imbalanced text classification by oversampling: correction of class predicted probability by prior probability

My dataset has 3 class and 900 examples for training. Class distribution is 255, 185, and 460. I found that if I oversample (random) the training data then I have to correct/calibrate the predicted ...
2
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0answers
84 views

Platt Scaling vs Isotonic Regression for reliability curve

I am learning classifier probability calibrations and have calibrated an eleastic net model using both Platt scaling and isotonic regression. As you can see in the attached image Platt scaling (on the ...
3
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1answer
37 views

I have 3 graphs of a binary Logistic Regression that I want to understand better what is happening and learn of a strategy to make the model better

My problem is the following: I have a binary Logistic Regression model with a very imbalanced dataset that outputs the percentage of the prediction. As can be seen in the images, as the threshold is ...
2
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2answers
168 views

Compare scores of models

We got several models with predictions. How can we compare scores of different models with each other? We assume that we got xgboost models and scores distribution can be different for each model, so ...
5
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2answers
2k views

convert predict_proba results using class_weight in training

As my dataset is unbalanced(class 1: 5%, class 0: 95%) I have used class_weight="balanced" parameter to train a random forest classification model. In this way I penalize the misclassification of a ...
4
votes
1answer
145 views

which loss function (if any) optimizes the calibration graph

The calibration graph is the predicted versus actual probability(see http://scikit-learn.org/stable/modules/generated/sklearn.calibration.calibration_curve.html). Is it possible to optimize the ...
7
votes
1answer
175 views

Are calibrated probabilities always more reliable?

EDIT: Based on the answer below, I have updated the question and added more detail. I have applied Dirichlet calibration to my fast-bert sentiment classification model, and I am struggling to really ...
1
vote
1answer
164 views

Adjust predicted probability after smote

i have an imbalance data set and I used smote to oversample the minority class and undersample the majority class. now, I want to check the test AUC using predict_proba of the model. I have two ...
1
vote
1answer
441 views

XGBoost probability distribution tending towards the extreme

I am using an XGBoost classifier to make risk predictions, and I see that even if it has very good binary classification results, the probability outputs are mainly under $0.05$ or over $0.95$ (like ...
3
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0answers
346 views

How to explain a Calibration Plot for many models?

I have a heavy imbalanced dataset with a classification problem. I try to plot the Calibration Curve from the sklearn.calibration package. In specific, I try the ...
1
vote
1answer
562 views

Probability calibration is worsening my model performance

I'm using RandomForest and XGBoost for binary classification, and my task is to predict probabilities for each class. Since tree-based models are bad with outputting usable probabilities, i imported ...
8
votes
1answer
5k views

Are the raw probabilities obtained from XGBoost, representative of the true underlying probabilties?

1) Is it feasible to use the raw probabilities obtained from XGBoost, e.g. probabilities obtained within the range of 0.4-0.5, as a true representation of approximately 40%-50% chance of an event ...
2
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1answer
299 views

Predict_proba on a binary classification problem

I have a binary classification task on my hands, i have a bunch of people that i need to classify as being ones or zeros and then use predict_proba to estimate how confident my prediction was on the ...
1
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2answers
488 views

Do I need to correct predict_proba by training fraction?

Many algorithms provide a predict_proba function indicating probability of a case to belong to that class (e.g. https://scikit-learn.org/stable/modules/generated/...
1
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0answers
19 views

How to evaluate a model based on the effect of one important variable?

We want a well-calibrated classifier that tells us the probability of an event. The model has multiple inputs, but we are interested in how the probability of an event changes as we vary one input. ...
1
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1answer
81 views

Does PMML support probability calibration?

As I need to port a decision tree model from Python to Java, I would like to know whether PMML (Predictive Model Markup Language) supports probability calibration.
1
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0answers
92 views

Probability Calibration : For 2D image data, how to use the calibration?

I have a model which takes 2D input data and does multi class classification in keras. I would like to plot the probability calibration curve. However, using the scikit function, it returns an error ...
1
vote
1answer
654 views

Calibrate the predicted class probability to make it represent a true probability?

Let's say that we have a simple binary classification model (a neural network -- NN) for classifying input images as "dog" ($y=1$) or "not dog" ($y=0$). Let's assume that the NN has one "sigmoid ...
6
votes
2answers
2k views

Probability Calibration : role of hidden layer in Neural Network

I try a simple Neural Network (Logistic Regression) to play with Keras. In input I have 5,000 features (output of a simple tf-idf vectorizer) and in the output layer I just use a random uniform ...