25 mins read
## Predicting Probabilities

## What Are ROC Curves?

## ROC Curves and AUC in Python

## What Are Precision-Recall Curves?

## Precision-Recall Curves in Python

## When to Use ROC vs. Precision-Recall Curves?

## ROC and Precision-Recall Curves With a Severe Imbalance

It can be more flexible to predict probabilities of an observation belonging to each class in a classification problem rather than predicting classes directly. This flexibility comes from the way that probabilities may be interpreted using different thresholds that allow the operator of the model to trade off concerns in the errors made by the model, such as the number of false positives compared to the number of false negatives. This is required when using models where the cost of one error outweighs the cost of other types of errors. Two diagnostic tools that help in the interpretation of probabilistic forecasts for binary (two-class) classification predictive modeling problems are **ROC Curves** and **Precision-Recall curves**.

In this tutorial, you will discover ROC Curves, Precision-Recall Curves, and when to use each to interpret the prediction of probabilities for binary classification problems.

In a classification problem, we may decide to predict the class values directly. Alternately, it can be more flexible to predict the probabilities for each class instead. The reason for this is to provide the capability to choose and even calibrate the threshold for how to interpret the predicted probabilities. For example, a default might be to use a threshold of 0.5, meaning that a probability in [0.0, 0.49] is a negative outcome (0) and a probability in [0.5, 1.0] is a positive outcome (1). This threshold can be adjusted to tune the behavior of the model for a specific problem. An example would be to reduce more of one or another type of error.

When making a prediction for a binary or two-class classification problem, there are two types of errors that we could make.

**False Positive**. Predict an event when there was no event.**False Negative**. Predict no event when in fact there was an event.

By predicting probabilities and calibrating a threshold, a balance of these two concerns can be chosen by the operator of the model. For example, in a smog prediction system, we may be far more concerned with having low false negatives than low false positives. A false negative would mean not warning about a smog day when in fact it is a high smog day, leading to health issues in the public that is unable to take precautions. A false positive means the public would take precautionary measures when they didn’t need to. A common way to compare models that predict probabilities for two-class problems is to use a ROC curve.

A useful tool when predicting the probability of a binary outcome is the Receiver Operating Characteristic curve or ROC curve. It is a plot of the false positive rate (x-axis) versus the true positive rate (y-axis) for a number of different candidate threshold values between 0.0 and 1.0. Put another way, it plots the false alarm rate versus the hit rate.

The true positive rate is calculated as the number of true positives divided by the sum of the number of true positives and the number of false negatives. It describes how good the model is at predicting the positive class when the actual outcome is positive.

True Positive Rate = True Positives / (True Positives + False Negatives)

The true positive rate is also referred to as sensitivity.

Sensitivity = True Positives / (True Positives + False Negatives)

The false positive rate is calculated as the number of false positives divided by the sum of the number of false positives and the number of true negatives. It is also called the false alarm rate as it summarizes how often a positive class is predicted when the actual outcome is negative.

False Positive Rate = False Positives / (False Positives + True Negatives)

The false positive rate is also referred to as the inverted specificity where specificity is the total number of true negatives divided by the sum of the number of true negatives and false positives.

Specificity = True Negatives / (True Negatives + False Positives)

Where:

False Positive Rate = 1 – Specificity

The ROC curve is a useful tool for a few reasons:

- The curves of different models can be compared directly in general or for different thresholds.
- The area under the curve (AUC) can be used as a summary of the model skill.

The shape of the curve contains a lot of information, including what we might care about most for a problem, the expected false positive rate, and the false negative rate.

To make this clear:

- Smaller values on the x-axis of the plot indicate lower false positives and higher true negatives.
- Larger values on the y-axis of the plot indicate higher true positives and lower false negatives.

If you are confused, remember, when we predict a binary outcome, it is either a correct prediction (true positive) or not (false positive). There is a tension between these options, the same with true negative and false negative values.

A skillful model will assign a higher probability to a randomly chosen real positive occurrence than a negative occurrence on average. This is what we mean when we say that the model has skill. Generally, skillful models are represented by curves that bow up to the top left of the plot. A no-skill classifier is one that cannot discriminate between the classes and would predict a random class or a constant class in all cases. A model with no skill is represented at the point (0.5, 0.5). A model with no skill at each threshold is represented by a diagonal line from the bottom left of the plot to the top right and has an AUC of 0.5. A model with perfect skill is represented at a point (0,1). A model with perfect skill is represented by a line that travels from the bottom left of the plot to the top left and then across the top to the top right. An operator may plot the ROC curve for the final model and choose a threshold that gives a desirable balance between the false positives and false negatives.

We can plot a ROC curve for a model in Python using the roc_curve() scikit-learn function. The function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. The function returns the false positive rates for each threshold, true positive rates for each threshold and thresholds. The AUC for the ROC can be calculated using the roc_auc_score() function. Like the roc_curve() function, the AUC function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. It returns the AUC score between 0.0 and 1.0 for no skill and perfect skill respectively. A complete example of calculating the ROC curve and ROC AUC for a Logistic Regression model on a small test problem is listed below.

```
# roc curve and auc
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from matplotlib import pyplot
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, random_state=1)
# split into train/test sets
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2)
# generate a no skill prediction (majority class)
ns_probs = [0 for _ in range(len(testy))]
# fit a model
model = LogisticRegression(solver='lbfgs')
model.fit(trainX, trainy)
# predict probabilities
lr_probs = model.predict_proba(testX)
# keep probabilities for the positive outcome only
lr_probs = lr_probs[:, 1]
# calculate scores
ns_auc = roc_auc_score(testy, ns_probs)
lr_auc = roc_auc_score(testy, lr_probs)
# summarize scores
print('No Skill: ROC AUC=%.3f' % (ns_auc))
print('Logistic: ROC AUC=%.3f' % (lr_auc))
# calculate roc curves
ns_fpr, ns_tpr, _ = roc_curve(testy, ns_probs)
lr_fpr, lr_tpr, _ = roc_curve(testy, lr_probs)
# plot the roc curve for the model
pyplot.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')
pyplot.plot(lr_fpr, lr_tpr, marker='.', label='Logistic')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
# show the legend
pyplot.legend()
# show the plot
pyplot.show()
```

Running the example prints the ROC AUC for the logistic regression model and the no skill classifier that only predicts 0 for all examples.

```
No Skill: ROC AUC=0.500
Logistic: ROC AUC=0.903
```

A plot of the ROC curve for the model is also created showing that the model has skill. Your results may vary given the stochastic nature of the algorithm or evaluation procedure or differences in numerical precision. Consider running the example a few times and comparing the average outcome.

There are many ways to evaluate the skill of a prediction model. An approach in the related field of information retrieval (finding documents based on queries) measures precision and recall. These measures are also useful in applied machine learning for evaluating binary classification models. Precision is a ratio of the number of true positives divided by the sum of the true positives and false positives. It describes how good a model is at predicting the positive class. Precision is referred to as the positive predictive value.

Positive Predictive Power = True Positives / (True Positives + False Positives)

or

Precision = True Positives / (True Positives + False Positives)

Recall is calculated as the ratio of the number of true positives divided by the sum of the true positives and the false negatives. Recall is the same as sensitivity.

Recall = True Positives / (True Positives + False Negatives)

or

Sensitivity = True Positives / (True Positives + False Negatives)

Recall == Sensitivity

Reviewing both precision and recall is useful in cases where there is an imbalance in the observations between the two classes. Specifically, there are many examples of no event (class 0) and only a few examples of an event (class 1). The reason for this is that typically the large number of class 0 examples means we are less interested in the skill of the model at predicting class 0 correctly, e.g. high true negatives. The key to the calculation of precision and recall is that the calculations *do not make use of the true negatives.* It is only concerned with the correct prediction of the minority class, class 1.

A precision-recall curve is a plot of the precision (y-axis) and the recall (x-axis) for different thresholds, much like the ROC curve. A no-skill classifier is one that cannot discriminate between the classes and would predict a random class or a constant class in all cases. The no-skill line changes based on the distribution of the positive to negative classes. *It is a horizontal line with the value of the ratio of positive cases in the dataset. For a balanced dataset, this is 0.5.*

**While the baseline is fixed with ROC, the baseline of [precision-recall curve] is determined by the ratio of positives (P) and negatives (N) as y = P / (P + N). For instance, we have y = 0.5 for a balanced class distribution …**

A model with perfect skill is depicted as a point at (1,1). A skilful model is represented by a curve that bows towards (1,1) above the flat line of no skill. There are also composite scores that attempt to summarize the precision and recall; two examples include:

**F-Measure**or F1 score: that calculates the harmonic mean of the precision and recall (harmonic mean because the precision and recall are rates).**Area Under Curve**: like the AUC, summarizes the integral or an approximation of the area under the precision-recall curve.

In terms of model selection, F-Measure summarizes model skill for a specific probability threshold (e.g. 0.5), whereas the area under the curve summarizes the skill of a model across thresholds, like ROC AUC. This makes precision-recall and a plot of precision vs. recall and summary measures useful tools for binary classification problems that have an imbalance in the observations for each class.

Precision and recall can be calculated in scikit-learn. The precision and recall can be calculated for thresholds using the precision_recall_curve() function that takes the true output values and the probabilities for the positive class as input and returns the precision, recall, and threshold values. The F-Measure can be calculated by calling the f1_score() function that takes the true class values and the predicted class values as arguments. The area under the precision-recall curve can be approximated by calling the auc() function and passing it the recall (x) and precision (y) values calculated for each threshold. When plotting precision and recall for each threshold as a curve, it is important that recall is provided as the x-axis and the precision is provided as the y-axis.

The complete example of calculating precision-recall curves for a Logistic Regression model is listed below.

```
# precision-recall curve and f1
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import f1_score
from sklearn.metrics import auc
from matplotlib import pyplot
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, random_state=1)
# split into train/test sets
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2)
# fit a model
model = LogisticRegression(solver='lbfgs')
model.fit(trainX, trainy)
# predict probabilities
lr_probs = model.predict_proba(testX)
# keep probabilities for the positive outcome only
lr_probs = lr_probs[:, 1]
# predict class values
yhat = model.predict(testX)
lr_precision, lr_recall, _ = precision_recall_curve(testy, lr_probs)
lr_f1, lr_auc = f1_score(testy, yhat), auc(lr_recall, lr_precision)
# summarize scores
print('Logistic: f1=%.3f auc=%.3f' % (lr_f1, lr_auc))
# plot the precision-recall curves
no_skill = len(testy[testy==1]) / len(testy)
pyplot.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
pyplot.plot(lr_recall, lr_precision, marker='.', label='Logistic')
# axis labels
pyplot.xlabel('Recall')
pyplot.ylabel('Precision')
# show the legend
pyplot.legend()
# show the plot
pyplot.show()
```

Running the example first prints the F1, area under the curve (AUC) for the logistic regression model.

```
Logistic: f1=0.841 auc=0.898
```

The precision-recall curve plot is then created showing the precision/recall for each threshold for a logistic regression model (orange) compared to a no-skill model (blue).

Generally, the use of ROC curves and precision-recall curves are as follows:

- ROC curves should be used when there are roughly equal numbers of observations for each class.
- Precision-Recall curves should be used when there
.*is a moderate to large class imbalance*

The reason for this recommendation is that ROC curves present an optimistic picture of the model on datasets with a class imbalance.

However, ROC curves can present an overly optimistic view of an algorithm’s performance if there is a large skew in the class distribution. […] Precision-Recall (PR) curves, often used in Information Retrieval , have been cited as an alternative to ROC curves for tasks with a large skew in the class distribution.

— The Relationship Between Precision-Recall and ROC Curves, 2006.

Some go further and suggest that using a ROC curve with an imbalanced dataset might be deceptive and lead to incorrect interpretations of the model skill.

[…] the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. [Precision-recall curve] plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions

— The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets, 2015.

The main reason for this optimistic picture is because of the use of true negatives in the False Positive Rate in the ROC Curve and the careful avoidance of this rate in the Precision-Recall curve.

If the proportion of positive to negative instances changes in a test set, the ROC curves will not change. Metrics such as accuracy, precision, lift and F scores use values from both columns of the confusion matrix. As a class distribution changes these measures will change as well, even if the fundamental classifier performance does not. ROC graphs are based upon TP rate and FP rate, in which each dimension is a strict columnar ratio, so do not depend on class distributions.

— ROC Graphs: Notes and Practical Considerations for Data Mining Researchers, 2003.

We can make this concrete with a short example. Below is the same ROC Curve example with a modified problem where there is a ratio of about 100:1 ratio of class=0 to class=1 observations (specifically Class0=985, Class1=15).

```
# roc curve and auc on an imbalanced dataset
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from matplotlib import pyplot
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.99,0.01], random_state=1)
# split into train/test sets
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2)
# generate a no skill prediction (majority class)
ns_probs = [0 for _ in range(len(testy))]
# fit a model
model = LogisticRegression(solver='lbfgs')
model.fit(trainX, trainy)
# predict probabilities
lr_probs = model.predict_proba(testX)
# keep probabilities for the positive outcome only
lr_probs = lr_probs[:, 1]
# calculate scores
ns_auc = roc_auc_score(testy, ns_probs)
lr_auc = roc_auc_score(testy, lr_probs)
# summarize scores
print('No Skill: ROC AUC=%.3f' % (ns_auc))
print('Logistic: ROC AUC=%.3f' % (lr_auc))
# calculate roc curves
ns_fpr, ns_tpr, _ = roc_curve(testy, ns_probs)
lr_fpr, lr_tpr, _ = roc_curve(testy, lr_probs)
# plot the roc curve for the model
pyplot.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')
pyplot.plot(lr_fpr, lr_tpr, marker='.', label='Logistic')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
# show the legend
pyplot.legend()
# show the plot
pyplot.show()
```

Running the example suggests that the model has skill.

```
No Skill: ROC AUC=0.500
Logistic: ROC AUC=0.716
```

Indeed, it has the skill, but all of that skill is measured as making correct true negative predictions and there are a lot of negative predictions to make. If you review the predictions, you will see that the model predicts the majority class (class 0) in all cases on the test set. The score is very misleading. A plot of the ROC Curve confirms the AUC interpretation of a skillful model for most probability thresholds.

We can also repeat the test of the same model on the same dataset and calculate a precision-recall curve and statistics instead. The complete example is listed below.

```
# precision-recall curve and f1 for an imbalanced dataset
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import f1_score
from sklearn.metrics import auc
from matplotlib import pyplot
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.99,0.01], random_state=1)
# split into train/test sets
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2)
# fit a model
model = LogisticRegression(solver='lbfgs')
model.fit(trainX, trainy)
# predict probabilities
lr_probs = model.predict_proba(testX)
# keep probabilities for the positive outcome only
lr_probs = lr_probs[:, 1]
# predict class values
yhat = model.predict(testX)
# calculate precision and recall for each threshold
lr_precision, lr_recall, _ = precision_recall_curve(testy, lr_probs)
# calculate scores
lr_f1, lr_auc = f1_score(testy, yhat), auc(lr_recall, lr_precision)
# summarize scores
print('Logistic: f1=%.3f auc=%.3f' % (lr_f1, lr_auc))
# plot the precision-recall curves
no_skill = len(testy[testy==1]) / len(testy)
pyplot.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
pyplot.plot(lr_recall, lr_precision, marker='.', label='Logistic')
# axis labels
pyplot.xlabel('Recall')
pyplot.ylabel('Precision')
# show the legend
pyplot.legend()
# show the plot
pyplot.show()
```

Running the example first prints the F1 and AUC scores. We can see that the model is penalized for predicting the majority class in all cases. The scores show that the model that looked good according to the ROC Curve is in fact barely skillful when considered using precision and recall that focuses on the positive class.

```
Logistic: f1=0.000 auc=0.054
```

The plot of the precision-recall curve highlights that the model is just barely above the no skill line for most thresholds. This is possible because the model predicts probabilities and is uncertain about some cases. These get exposed through the different thresholds evaluated in the construction of the curve, flipping some class 0 to class 1, offering some precision but very low recall.

In this section, we will explore the case of using the ROC Curves and Precision-Recall curves with a binary classification problem that has a severe class imbalance. Firstly, we can use the *make_classification()* function to create 1,000 examples for a classification problem with about a 1:100 minority to majority class ratio. This can be achieved by setting the “*weights*” argument and specifying the weighting of generated instances from each class. We will use a 99 percent and 1 percent weighting with 1,000 total examples, meaning there would be about 990 for class 0 and about 10 for class 1. We can then split the dataset into training and test sets and ensure that both have the same general class ratio by setting the “*stratify*” argument on the call to the *train_test_split()* function and setting it to the array of target variables. ُhe complete example of preparing the imbalanced dataset is listed below.

```
# create an imbalanced dataset
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.99, 0.01], random_state=1)
# split into train/test sets with same class ratio
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2, stratify=y)
# summarize dataset
print('Dataset: Class0=%d, Class1=%d' % (len(y[y==0]), len(y[y==1])))
print('Train: Class0=%d, Class1=%d' % (len(trainy[trainy==0]), len(trainy[trainy==1])))
print('Test: Class0=%d, Class1=%d' % (len(testy[testy==0]), len(testy[testy==1])))
```

Running the example first summarizes the class ratio of the whole dataset, then the ratio for each of the train and test sets, confirming the split of the dataset holds the same ratio.

```
Dataset: Class0=985, Class1=15
Train: Class0=492, Class1=8
Test: Class0=493, Class1=7
```

Next, we can develop a Logistic Regression model on the dataset and evaluate the performance of the model using a ROC Curve and ROC AUC score, and compare the results to a no-skill classifier, as we did in a prior section.

```
# roc curve and roc auc on an imbalanced dataset
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.dummy import DummyClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from matplotlib import pyplot
# plot no skill and model roc curves
def plot_roc_curve(test_y, naive_probs, model_probs):
# plot naive skill roc curve
fpr, tpr, _ = roc_curve(test_y, naive_probs)
pyplot.plot(fpr, tpr, linestyle='--', label='No Skill')
# plot model roc curve
fpr, tpr, _ = roc_curve(test_y, model_probs)
pyplot.plot(fpr, tpr, marker='.', label='Logistic')
# axis labels
pyplot.xlabel('False Positive Rate')
pyplot.ylabel('True Positive Rate')
# show the legend
pyplot.legend()
# show the plot
pyplot.show()
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.99, 0.01], random_state=1)
# split into train/test sets with same class ratio
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2, stratify=y)
# no skill model, stratified random class predictions
model = DummyClassifier(strategy='stratified')
model.fit(trainX, trainy)
yhat = model.predict_proba(testX)
naive_probs = yhat[:, 1]
# calculate roc auc
roc_auc = roc_auc_score(testy, naive_probs)
print('No Skill ROC AUC %.3f' % roc_auc)
# skilled model
model = LogisticRegression(solver='lbfgs')
model.fit(trainX, trainy)
yhat = model.predict_proba(testX)
model_probs = yhat[:, 1]
# calculate roc auc
roc_auc = roc_auc_score(testy, model_probs)
print('Logistic ROC AUC %.3f' % roc_auc)
# plot roc curves
plot_roc_curve(testy, naive_probs, model_probs)
```

The ROC AUC scores for both classifiers are reported, showing the no-skill classifier achieving the lowest score of approximately 0.5 as expected. The results for the logistic regression model suggest it has some skill with a score of about 0.869.

```
No Skill ROC AUC 0.490
Logistic ROC AUC 0.869
```

A ROC curve is also created for the model and the no skill classifier, showing not excellent performance, but definitely skillful performance as compared to the diagonal no skill.

Next, we can perform an analysis of the same model fit and evaluate on the same data using the precision-recall curve and AUC score.

```
# pr curve and pr auc on an imbalanced dataset
from sklearn.datasets import make_classification
from sklearn.dummy import DummyClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_recall_curve
from sklearn.metrics import auc
from matplotlib import pyplot
# plot no skill and model precision-recall curves
def plot_pr_curve(test_y, model_probs):
# calculate the no skill line as the proportion of the positive class
no_skill = len(test_y[test_y==1]) / len(test_y)
# plot the no skill precision-recall curve
pyplot.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')
# plot model precision-recall curve
precision, recall, _ = precision_recall_curve(testy, model_probs)
pyplot.plot(recall, precision, marker='.', label='Logistic')
# axis labels
pyplot.xlabel('Recall')
pyplot.ylabel('Precision')
# show the legend
pyplot.legend()
# show the plot
pyplot.show()
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.99, 0.01], random_state=1)
# split into train/test sets with same class ratio
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2, stratify=y)
# no skill model, stratified random class predictions
model = DummyClassifier(strategy='stratified')
model.fit(trainX, trainy)
yhat = model.predict_proba(testX)
naive_probs = yhat[:, 1]
# calculate the precision-recall auc
precision, recall, _ = precision_recall_curve(testy, naive_probs)
auc_score = auc(recall, precision)
print('No Skill PR AUC: %.3f' % auc_score)
# fit a model
model = LogisticRegression(solver='lbfgs')
model.fit(trainX, trainy)
yhat = model.predict_proba(testX)
model_probs = yhat[:, 1]
# calculate the precision-recall auc
precision, recall, _ = precision_recall_curve(testy, model_probs)
auc_score = auc(recall, precision)
print('Logistic PR AUC: %.3f' % auc_score)
# plot precision-recall curves
plot_pr_curve(testy, model_probs)
```

In this case, we can see that the Logistic Regression model achieves a PR AUC of about 0.228 and a no skill model achieves a PR AUC of about 0.007.

```
No Skill PR AUC: 0.007
Logistic PR AUC: 0.228
```

We can see the horizontal line of the no skill classifier as expected and in this case the zig-zag line of the logistic regression curve is close to the no skill line.

To explain why the ROC and PR curves tell a different story, recall that the PR curve focuses on the minority class, whereas the ROC curve covers both classes. If we use a threshold of 0.5 and use the logistic regression model to make a prediction for all examples in the test set, we see that it predicts class 0 or the majority class in all cases. This can be confirmed by using the fit model to predict crisp class labels, that will use the default threshold of 0.5. The distribution of predicted class labels can then be summarized.

We can then create a histogram of the predicted probabilities of the positive class to confirm that the mass of predicted probabilities is below 0.5, and therefore mapped to class 0.

```
# summarize the distribution of predicted probabilities
from collections import Counter
from matplotlib import pyplot
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
# generate 2 class dataset
X, y = make_classification(n_samples=1000, n_classes=2, weights=[0.99, 0.01], random_state=1)
# split into train/test sets with same class ratio
trainX, testX, trainy, testy = train_test_split(X, y, test_size=0.5, random_state=2, stratify=y)
# fit a model
model = LogisticRegression(solver='lbfgs')
model.fit(trainX, trainy)
# predict probabilities
yhat = model.predict_proba(testX)
# retrieve just the probabilities for the positive class
pos_probs = yhat[:, 1]
# predict class labels
yhat = model.predict(testX)
# summarize the distribution of class labels
print(Counter(yhat))
# create a histogram of the predicted probabilities
pyplot.hist(pos_probs, bins=100)
pyplot.show()
```

Running the example first summarizes the distribution of predicted class labels. As we expected, the majority class (class 0) is predicted for all examples in the test set.

```
Counter({0: 500})
```

A histogram plot of the predicted probabilities for class 1 is also created, showing the center of mass (most predicted probabilities) is less than 0.5 and in fact is generally close to zero.

This means, that unless the probability threshold is carefully chosen, any skillful nuance in the predictions made by the model will be lost. Selecting thresholds used to interpret predicted probabilities as crisp class labels is an important topic

Resources: