Classification - Introduction

Introduction to Classification

Classification may be defined as the process of predicting class or category from observed values or given data points. The categorized output can have the form such as “Black” or “White” or “spam” or “no spam”.

Mathematically, classification is the task of approximating a mapping function (f) from input variables (X) to output variables (Y). It is basically belongs to the supervised machine learning in which targets are also provided along with the input data set.

An example of classification problem can be the spam detection in emails. There can be only two categories of output, “spam” and “no spam”; hence this is a binary type classification.

To implement this classification, we first need to train the classifier. For this example, “spam” and “no spam” emails would be used as the training data. After successfully train the classifier, it can be used to detect an unknown email.

Types of Learners in Classification

We have two types of learners in respective to classification problems −

Lazy Learners

As the name suggests, such kind of learners waits for the testing data to be appeared after storing the training data. Classification is done only after getting the testing data. They spend less time on training but more time on predicting. Examples of lazy learners are K-nearest neighbor and case-based reasoning.

Eager Learners

As opposite to lazy learners, eager learners construct classification model without waiting for the testing data to be appeared after storing the training data. They spend more time on training but less time on predicting. Examples of eager learners are Decision Trees, Naïve Bayes and Artificial Neural Networks (ANN).

Building a Classifier in Python

Scikit-learn, a Python pbrary for machine learning can be used to build a classifier in Python. The steps for building a classifier in Python are as follows −

Step1: Importing necessary python package

For building a classifier using scikit-learn, we need to import it. We can import it by using following script −

import sklearn

Step2: Importing dataset

After importing necessary package, we need a dataset to build classification prediction model. We can import it from sklearn dataset or can use other one as per our requirement. We are going to use sklearn’s Breast Cancer Wisconsin Diagnostic Database. We can import it with the help of following script −

from sklearn.datasets import load_breast_cancer

The following script will load the dataset;

data = load_breast_cancer()

We also need to organize the data and it can be done with the help of following scripts −

label_names = data[ target_names ]
   labels = data[ target ]
   feature_names = data[ feature_names ]
   features = data[ data ]

The following command will print the name of the labels, ‘mapgnant’ and ‘benign’ in case of our database.

print(label_names)

The output of the above command is the names of the labels −

[ mapgnant   benign ]

These labels are mapped to binary values 0 and 1. Mapgnant cancer is represented by 0 and Benign cancer is represented by 1.

The feature names and feature values of these labels can be seen with the help of following commands −

print(feature_names[0])

The output of the above command is the names of the features for label 0 i.e. Mapgnant cancer −

mean radius

Similarly, names of the features for label can be produced as follows −

print(feature_names[1])

The output of the above command is the names of the features for label 1 i.e. Benign cancer −

mean texture

We can print the features for these labels with the help of following command −

print(features[0])

This will give the following output −

[
   1.799e+01 1.038e+01 1.228e+02 1.001e+03 1.184e-01 2.776e-01 3.001e-01
   1.471e-01 2.419e-01 7.871e-02 1.095e+00 9.053e-01 8.589e+00 1.534e+02
   6.399e-03 4.904e-02 5.373e-02 1.587e-02 3.003e-02 6.193e-03 2.538e+01
   1.733e+01 1.846e+02 2.019e+03 1.622e-01 6.656e-01 7.119e-01 2.654e-01
   4.601e-01 1.189e-01
]

We can print the features for these labels with the help of following command −

print(features[1])

This will give the following output −

[
   2.057e+01 1.777e+01 1.329e+02 1.326e+03 8.474e-02 7.864e-02 8.690e-02
   7.017e-02 1.812e-01 5.667e-02 5.435e-01 7.339e-01 3.398e+00 7.408e+01
   5.225e-03 1.308e-02 1.860e-02 1.340e-02 1.389e-02 3.532e-03 2.499e+01
   2.341e+01 1.588e+02 1.956e+03 1.238e-01 1.866e-01 2.416e-01 1.860e-01
   2.750e-01 8.902e-02
]

Step3: Organizing data into training & testing sets

As we need to test our model on unseen data, we will spanide our dataset into two parts: a training set and a test set. We can use train_test_sppt() function of sklearn python package to sppt the data into sets. The following command will import the function −

from sklearn.model_selection import train_test_sppt

Now, next command will sppt the data into training & testing data. In this example, we are using taking 40 percent of the data for testing purpose and 60 percent of the data for training purpose −

train, test, train_labels, test_labels = train_test_sppt(
   features,labels,test_size = 0.40, random_state = 42
)

Step4: Model evaluation

After spaniding the data into training and testing we need to build the model. We will be using Naïve Bayes algorithm for this purpose. The following commands will import the GaussianNB module −

from sklearn.naive_bayes import GaussianNB

Now, initiapze the model as follows −

gnb = GaussianNB()

Next, with the help of following command we can train the model −

model = gnb.fit(train, train_labels)

Now, for evaluation purpose we need to make predictions. It can be done by using predict() function as follows −

preds = gnb.predict(test)
print(preds)

This will give the following output −

[
   1 0 0 1 1 0 0 0 1 1 1 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1
   0 1 1 0 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 1 1 0 0 1 1 1 0 0 1 1 0 0 1 0 1 1
   1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 0 1 1 0 1 1 0
   0 0 1 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 1 1 1 0 1 1 0 0 1 0 1 1 0 1 0 0 1 1 1 1
   1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 0 
   1 0 1 1 1 1 0 1 1 0 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 0 0 1 1 0 
   1

]

The above series of 0s and 1s in output are the predicted values for the Mapgnant and Benign tumor classes.

Step5: Finding accuracy

We can find the accuracy of the model build in previous step by comparing the two arrays namely test_labels and preds. We will be using the accuracy_score() function to determine the accuracy.

from sklearn.metrics import accuracy_score
   print(accuracy_score(test_labels,preds))
   0.951754385965

The above output shows that NaïveBayes classifier is 95.17% accurate .

Classification Evaluation Metrics

The job is not done even if you have finished implementation of your Machine Learning apppcation or model. We must have to find out how effective our model is? There can be different evaluation metrics, but we must choose it carefully because the choice of metrics influences how the performance of a machine learning algorithm is measured and compared.

The following are some of the important classification evaluation metrics among which you can choose based upon your dataset and kind of problem −

Confusion Matrix

It is the easiest way to measure the performance of a classification problem where the output can be of two or more type of classes. A confusion matrix is nothing but a table with two dimensions viz. “Actual” and “Predicted” and furthermore, both the dimensions have “True Positives (TP)”, “True Negatives (TN)”, “False Positives (FP)”, “False Negatives (FN)” as shown below −

The explanation of the terms associated with confusion matrix are as follows −

True Positives (TP) − It is the case when both actual class & predicted class of data point is 1.

True Negatives (TN) − It is the case when both actual class & predicted class of data point is 0.

False Positives (FP) − It is the case when actual class of data point is 0 & predicted class of data point is 1.

False Negatives (FN) − It is the case when actual class of data point is 1 & predicted class of data point is 0.

We can find the confusion matrix with the help of confusion_matrix() function of sklearn. With the help of the following script, we can find the confusion matrix of above built binary classifier −

from sklearn.metrics import confusion_matrix

Output

[
   [ 73   7]
   [  4 144]
]

Accuracy

It may be defined as the number of correct predictions made by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

For above built binary classifier, TP + TN = 73+144 = 217 and TP+FP+FN+TN = 73+7+4+144=228.

Hence, Accuracy = 217/228 = 0.951754385965 which is same as we have calculated after creating our binary classifier.

Precision

Precision, used in document retrievals, may be defined as the number of correct documents returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

For the above built binary classifier, TP = 73 and TP+FP = 73+7 = 80.

Hence, Precision = 73/80 = 0.915

Recall or Sensitivity

Recall may be defined as the number of positives returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

For above built binary classifier, TP = 73 and TP+FN = 73+4 = 77.

Hence, Precision = 73/77 = 0.94805

Specificity

Specificity, in contrast to recall, may be defined as the number of negatives returned by our ML model. We can easily calculate it by confusion matrix with the help of following formula −

For the above built binary classifier, TN = 144 and TN+FP = 144+7 = 151.

Hence, Precision = 144/151 = 0.95364

Various ML Classification Algorithms

The followings are some important ML classification algorithms −

Logistic Regression

Support Vector Machine (SVM)

Decision Tree

Naïve Bayes

Random Forest

We will be discussing all these classification algorithms in detail in further chapters.

Apppcations

Some of the most important apppcations of classification algorithms are as follows −

Speech Recognition

Handwriting Recognition

Biometric Identification

Document Classification