09_classification_metrics


Evaluating a classification model

From the video series: Introduction to machine learning with scikit-learn

Agenda

  • What is the purpose of model evaluation, and what are some common evaluation procedures?
  • What is the usage of classification accuracy, and what are its limitations?
  • How does a confusion matrix describe the performance of a classifier?
  • What metrics can be computed from a confusion matrix?
  • How can you adjust classifier performance by changing the classification threshold?
  • What is the purpose of an ROC curve?
  • How does Area Under the Curve (AUC) differ from classification accuracy?

Review of model evaluation

  • Need a way to choose between models: different model types, tuning parameters, and features
  • Use a model evaluation procedure to estimate how well a model will generalize to out-of-sample data
  • Requires a model evaluation metric to quantify the model performance

Model evaluation procedures

  1. Training and testing on the same data
    • Rewards overly complex models that "overfit" the training data and won't necessarily generalize
  2. Train/test split
    • Split the dataset into two pieces, so that the model can be trained and tested on different data
    • Better estimate of out-of-sample performance, but still a "high variance" estimate
    • Useful due to its speed, simplicity, and flexibility
  3. K-fold cross-validation
    • Systematically create "K" train/test splits and average the results together
    • Even better estimate of out-of-sample performance
    • Runs "K" times slower than train/test split

Model evaluation metrics

  • Regression problems: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error
  • Classification problems: Classification accuracy

Classification accuracy

Pima Indian Diabetes dataset from the UCI Machine Learning Repository


In [2]:
# read the data into a Pandas DataFrame
import pandas as pd
url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data'
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
pima = pd.read_csv(url, header=None, names=col_names)

In [3]:
# print the first 5 rows of data
pima.head()


Out[3]:
pregnant glucose bp skin insulin bmi pedigree age label
0 6 148 72 35 0 33.6 0.627 50 1
1 1 85 66 29 0 26.6 0.351 31 0
2 8 183 64 0 0 23.3 0.672 32 1
3 1 89 66 23 94 28.1 0.167 21 0
4 0 137 40 35 168 43.1 2.288 33 1

Question: Can we predict the diabetes status of a patient given their health measurements?


In [4]:
# define X and y
feature_cols = ['pregnant', 'insulin', 'bmi', 'age']
X = pima[feature_cols]
y = pima.label

In [5]:
# split X and y into training and testing sets
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

In [6]:
# train a logistic regression model on the training set
from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)


Out[6]:
LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr',
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0)

In [7]:
# make class predictions for the testing set
y_pred_class = logreg.predict(X_test)

Classification accuracy: percentage of correct predictions


In [8]:
# calculate accuracy
from sklearn import metrics
print metrics.accuracy_score(y_test, y_pred_class)


0.692708333333

Null accuracy: accuracy that could be achieved by always predicting the most frequent class


In [9]:
# examine the class distribution of the testing set (using a Pandas Series method)
y_test.value_counts()


Out[9]:
0    130
1     62
dtype: int64

In [10]:
# calculate the percentage of ones
y_test.mean()


Out[10]:
0.3229166666666667

In [11]:
# calculate the percentage of zeros
1 - y_test.mean()


Out[11]:
0.6770833333333333

In [12]:
# calculate null accuracy (for binary classification problems coded as 0/1)
max(y_test.mean(), 1 - y_test.mean())


Out[12]:
0.6770833333333333

In [13]:
# calculate null accuracy (for multi-class classification problems)
y_test.value_counts().head(1) / len(y_test)


Out[13]:
0    0.677083
dtype: float64

Comparing the true and predicted response values


In [14]:
# print the first 25 true and predicted responses
print 'True:', y_test.values[0:25]
print 'Pred:', y_pred_class[0:25]


True: [1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0]
Pred: [0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]

Conclusion:

  • Classification accuracy is the easiest classification metric to understand
  • But, it does not tell you the underlying distribution of response values
  • And, it does not tell you what "types" of errors your classifier is making

Confusion matrix

Table that describes the performance of a classification model


In [15]:
# IMPORTANT: first argument is true values, second argument is predicted values
print metrics.confusion_matrix(y_test, y_pred_class)


[[118  12]
 [ 47  15]]

  • Every observation in the testing set is represented in exactly one box
  • It's a 2x2 matrix because there are 2 response classes
  • The format shown here is not universal

Basic terminology

  • True Positives (TP): we correctly predicted that they do have diabetes
  • True Negatives (TN): we correctly predicted that they don't have diabetes
  • False Positives (FP): we incorrectly predicted that they do have diabetes (a "Type I error")
  • False Negatives (FN): we incorrectly predicted that they don't have diabetes (a "Type II error")

In [16]:
# print the first 25 true and predicted responses
print 'True:', y_test.values[0:25]
print 'Pred:', y_pred_class[0:25]


True: [1 0 0 1 0 0 1 1 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 0 0]
Pred: [0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0]

In [17]:
# save confusion matrix and slice into four pieces
confusion = metrics.confusion_matrix(y_test, y_pred_class)
TP = confusion[1, 1]
TN = confusion[0, 0]
FP = confusion[0, 1]
FN = confusion[1, 0]

Metrics computed from a confusion matrix

Classification Accuracy: Overall, how often is the classifier correct?


In [18]:
print (TP + TN) / float(TP + TN + FP + FN)
print metrics.accuracy_score(y_test, y_pred_class)


0.692708333333
0.692708333333

Classification Error: Overall, how often is the classifier incorrect?

  • Also known as "Misclassification Rate"

In [19]:
print (FP + FN) / float(TP + TN + FP + FN)
print 1 - metrics.accuracy_score(y_test, y_pred_class)


0.307291666667
0.307291666667

Sensitivity: When the actual value is positive, how often is the prediction correct?

  • How "sensitive" is the classifier to detecting positive instances?
  • Also known as "True Positive Rate" or "Recall"

In [20]:
print TP / float(TP + FN)
print metrics.recall_score(y_test, y_pred_class)


0.241935483871
0.241935483871

Specificity: When the actual value is negative, how often is the prediction correct?

  • How "specific" (or "selective") is the classifier in predicting positive instances?

In [21]:
print TN / float(TN + FP)


0.907692307692

False Positive Rate: When the actual value is negative, how often is the prediction incorrect?


In [22]:
print FP / float(TN + FP)


0.0923076923077

Precision: When a positive value is predicted, how often is the prediction correct?

  • How "precise" is the classifier when predicting positive instances?

In [23]:
print TP / float(TP + FP)
print metrics.precision_score(y_test, y_pred_class)


0.555555555556
0.555555555556

Many other metrics can be computed: F1 score, Matthews correlation coefficient, etc.

Conclusion:

  • Confusion matrix gives you a more complete picture of how your classifier is performing
  • Also allows you to compute various classification metrics, and these metrics can guide your model selection

Which metrics should you focus on?

  • Choice of metric depends on your business objective
  • Spam filter (positive class is "spam"): Optimize for precision or specificity because false negatives (spam goes to the inbox) are more acceptable than false positives (non-spam is caught by the spam filter)
  • Fraudulent transaction detector (positive class is "fraud"): Optimize for sensitivity because false positives (normal transactions that are flagged as possible fraud) are more acceptable than false negatives (fraudulent transactions that are not detected)

Adjusting the classification threshold


In [24]:
# print the first 10 predicted responses
logreg.predict(X_test)[0:10]


Out[24]:
array([0, 0, 0, 0, 0, 0, 0, 1, 0, 1], dtype=int64)

In [25]:
# print the first 10 predicted probabilities of class membership
logreg.predict_proba(X_test)[0:10, :]


Out[25]:
array([[ 0.63247571,  0.36752429],
       [ 0.71643656,  0.28356344],
       [ 0.71104114,  0.28895886],
       [ 0.5858938 ,  0.4141062 ],
       [ 0.84103973,  0.15896027],
       [ 0.82934844,  0.17065156],
       [ 0.50110974,  0.49889026],
       [ 0.48658459,  0.51341541],
       [ 0.72321388,  0.27678612],
       [ 0.32810562,  0.67189438]])

In [26]:
# print the first 10 predicted probabilities for class 1
logreg.predict_proba(X_test)[0:10, 1]


Out[26]:
array([ 0.36752429,  0.28356344,  0.28895886,  0.4141062 ,  0.15896027,
        0.17065156,  0.49889026,  0.51341541,  0.27678612,  0.67189438])

In [27]:
# store the predicted probabilities for class 1
y_pred_prob = logreg.predict_proba(X_test)[:, 1]

In [28]:
# allow plots to appear in the notebook
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['font.size'] = 14

In [29]:
# histogram of predicted probabilities
plt.hist(y_pred_prob, bins=8)
plt.xlim(0, 1)
plt.title('Histogram of predicted probabilities')
plt.xlabel('Predicted probability of diabetes')
plt.ylabel('Frequency')


Out[29]:
<matplotlib.text.Text at 0x18b01dd8>

Decrease the threshold for predicting diabetes in order to increase the sensitivity of the classifier


In [30]:
# predict diabetes if the predicted probability is greater than 0.3
from sklearn.preprocessing import binarize
y_pred_class = binarize(y_pred_prob, 0.3)[0]

In [31]:
# print the first 10 predicted probabilities
y_pred_prob[0:10]


Out[31]:
array([ 0.36752429,  0.28356344,  0.28895886,  0.4141062 ,  0.15896027,
        0.17065156,  0.49889026,  0.51341541,  0.27678612,  0.67189438])

In [32]:
# print the first 10 predicted classes with the lower threshold
y_pred_class[0:10]


Out[32]:
array([ 1.,  0.,  0.,  1.,  0.,  0.,  1.,  1.,  0.,  1.])

In [33]:
# previous confusion matrix (default threshold of 0.5)
print confusion


[[118  12]
 [ 47  15]]

In [34]:
# new confusion matrix (threshold of 0.3)
print metrics.confusion_matrix(y_test, y_pred_class)


[[80 50]
 [16 46]]

In [35]:
# sensitivity has increased (used to be 0.24)
print 46 / float(46 + 16)


0.741935483871

In [36]:
# specificity has decreased (used to be 0.91)
print 80 / float(80 + 50)


0.615384615385

Conclusion:

  • Threshold of 0.5 is used by default (for binary problems) to convert predicted probabilities into class predictions
  • Threshold can be adjusted to increase sensitivity or specificity
  • Sensitivity and specificity have an inverse relationship

ROC Curves and Area Under the Curve (AUC)

Question: Wouldn't it be nice if we could see how sensitivity and specificity are affected by various thresholds, without actually changing the threshold?

Answer: Plot the ROC curve!


In [37]:
# IMPORTANT: first argument is true values, second argument is predicted probabilities
fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_prob)
plt.plot(fpr, tpr)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.title('ROC curve for diabetes classifier')
plt.xlabel('False Positive Rate (1 - Specificity)')
plt.ylabel('True Positive Rate (Sensitivity)')
plt.grid(True)


  • ROC curve can help you to choose a threshold that balances sensitivity and specificity in a way that makes sense for your particular context
  • You can't actually see the thresholds used to generate the curve on the ROC curve itself

In [38]:
# define a function that accepts a threshold and prints sensitivity and specificity
def evaluate_threshold(threshold):
    print 'Sensitivity:', tpr[thresholds > threshold][-1]
    print 'Specificity:', 1 - fpr[thresholds > threshold][-1]

In [39]:
evaluate_threshold(0.5)


Sensitivity: 0.241935483871
Specificity: 0.907692307692

In [40]:
evaluate_threshold(0.3)


Sensitivity: 0.741935483871
Specificity: 0.615384615385

AUC is the percentage of the ROC plot that is underneath the curve:


In [41]:
# IMPORTANT: first argument is true values, second argument is predicted probabilities
print metrics.roc_auc_score(y_test, y_pred_prob)


0.724565756824
  • AUC is useful as a single number summary of classifier performance.
  • If you randomly chose one positive and one negative observation, AUC represents the likelihood that your classifier will assign a higher predicted probability to the positive observation.
  • AUC is useful even when there is high class imbalance (unlike classification accuracy).

In [42]:
# calculate cross-validated AUC
from sklearn.cross_validation import cross_val_score
cross_val_score(logreg, X, y, cv=10, scoring='roc_auc').mean()


Out[42]:
0.73782336182336183

Confusion matrix advantages:

  • Allows you to calculate a variety of metrics
  • Useful for multi-class problems (more than two response classes)

ROC/AUC advantages:

  • Does not require you to set a classification threshold
  • Still useful when there is high class imbalance

Confusion Matrix Resources

ROC and AUC Resources

Other Resources

Comments or Questions?


In [1]:
from IPython.core.display import HTML
def css_styling():
    styles = open("styles/custom.css", "r").read()
    return HTML(styles)
css_styling()


Out[1]: