Machine Learning (ML) algorithms are used to classify tasks by inferring patterns in data. This approach is used by numerous Google products, including Gmail and Twitter for analysing message sentiment, and Google Lens, which categorises different plant species based on their images.
Multiple Categories for Classification
Multiclass categorisation typically involves the following primary categories:
- Binary Categorisation, or classification into two separate classes
- Categorisation based on multiple criteria
- Assigning a category based on a set of labels
Dividing into Two Groups or “Bins”
Binary classification refers to categorisation of data into two distinct categories or “buckets.” This method can be used to determine whether a patient has tuberculosis or not, indicated by 1 or 0 respectively, or to label a movie review as positive or negative, again indicated by 1 or 0.
Categorisation Based on Multiple Criteria
Multiclass categorisation is used to classify data into more than two distinct groups. For example, if we need to recognise a digit in an image and assign it a number between zero and nine, we use multiclass classification. Similarly, if we offer ten different courses, we would use multiclass classification to assign a single category label to each course. Furthermore, if we had to rank songs from one to five in order of popularity, we can also employ multiclass classification.
Categorising Based on Multiple Labels
A single set of data can be interpreted in several ways. For instance, a single image could feature both a house and a plant, and to accurately identify each object, distinct labelling systems and appropriate machine learning algorithms must be developed. The initial phase of this process involves selecting the most appropriate categorization option for the given situation.
This blog post will delve deeply into multiclass classification for machine learning tasks.
Machine Learning Techniques for Multiclass Classification in Python
To detect patterns, machine learning is a potent tool for multiclass classification, where algorithms are trained using previously classified data. Various machine learning algorithms can be employed, such as logistic regression and support vector machines (SVMs), which are particularly effective for binary classification.
Despite being limited to binary classification, we can handle multiclass classification problems by leveraging Support Vector Machines (SVM). We can do this by using either a one-on-one or all-versus-one approach, wherein multiple binary classifiers are utilised.
The next section of this blog post will further explore various machine learning (ML) techniques. These techniques include Naive Bayes classification, decision trees, and K Nearest Neighbours (KNN). We will conduct a thorough investigation of each of these methods.
The Winner-Takes-All Approach
A multiclass classification problem with four classes (i.e. dogs, cats, cows, and pigs) can be simplified using a straightforward technique. This technique involves breaking down the four classes into two separate groups and performing two binary classifications on these groups. For instance, one binary classification task would involve categorising images of dogs and cats, while the second binary classification would involve categorising pictures of cows and pigs. This technique can simplify multiclass classifications involving any number of classes.
To precisely determine if an image contains a particular subject, we can employ four distinct binary classifiers, each designed for one of the four distinct classes. For instance, one classifier can confidently identify whether a picture contains a dog, while a second classifier can do the same for a cat. This pattern can be repeated for the other two classes.
Finally, the prediction class with the highest confidence level will be selected. This can be accomplished using individual models of binary classification, such as logistic regression. Luckily, implementing this is easy and convenient using the sklearn library.
Below are the instructions for creating a synthetic dataset that can be used to test the models mentioned above. The
make_classification
function will generate a dataset with the inputs of our choice – features, classes, and how many samples we want.
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from sklearn.datasets import make_classification
x, y = make_classification(n_samples=3000, n_features=12, n_informative=5, n_redundant=5, n_classes=4, random_state=36)
To employ the one-vs-all strategy, we can use sklearn’s logistic regression model with the ‘multi-class’ option set to ‘ovr.’ This approach is based on the concept of “one versus the rest,” abbreviated as “ovr,” and offers an efficient solution to the problem.
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from sklearn.linear_model import LogisticRegression
model = LogisticRegression(multi_class='ovr')
model.fit(X, y)
predictions = model.predict(X)
The above code snippet illustrates how to train the model and make predictions on the dataset.
The matches are played one-on-one.
This strategy is akin to the previous one, but with a significant distinction; rather than using a single binary classifier, we will train separate binary classifiers for each class, factoring in all feasible combinations.
One could use the binary classification technique to distinguish between the four domestic animal groups: dogs, cats, cows, and pigs. To achieve this, we would create and train six distinct binary classifiers, as explained below.
Qualifier #1: Comparing Dogs and Cats
Separator 2: Cow vs. Dog
Third Classification Criterion: Pig vs. Dog
Fourth Separator: Dog versus Pig
Differentiator 5: Battle of the Sexes: Cats vs. Pigs
Sorting Rule No. 6: Swine versus Bovine
To calculate the number of binary classifiers needed for an ‘n’-class multiclass classification, multiply n by (n-1) and divide the result by two. It’s crucial to remember that this method entails comparing more models than the one-vs-all approach. A range of classifiers can be used, ranging from logistic regression and support vector machines to k-nearest neighbour and more.
The implementation requires functions from the multiclass module of the sklearn package.
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from sklearn.multiclass import OneVsOneClassifier
svc_model=SVC()
ovo_model=OneVsOneClassifier(svc_model)
ovo_model.fit(x,y)
As we delve deeper into the topic, let’s examine some renowned algorithms that have demonstrated success in various domains, including business assignments, Kaggle contests, hackathons, and others. Utilizing these algorithms can provide us with valuable insights and a deeper comprehension of the available data.
Decision Tree-based Classifiers
The decision tree is a predictive model that helps in identifying a set of decisions and their corresponding outcomes. In this methodology, pre-determined criteria are utilized to partition and divide the dataset into subsets at each level of the decision tree. The purpose of this process is to group samples that possess similar statistical properties. This approach assists in identifying patterns and correlations in the data, which can ultimately contribute to making well-informed predictions.
To select the optimal information division, we must take into account both the entropy and the data’s associated knowledge. The decision tree’s primary objective is to create a partition that minimizes the data’s entropy level, which represents the data’s randomness.
The starting points from where decisions must be made are referred to as the root nodes of a decision tree. Decision nodes are subsequent nodes that require choices to be made. The eventual output of the tree is provided by the leaf nodes located at the end of the tree.
In this instance, we will use the sklearn package to generate a dataset with three categories and train a decision tree classifier.
Please provide the content you would like me to rephrase.
x, y = make_classification(n_samples=500, n_features=5, n_informative=4, n_redundant=1, n_classes=3)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1121218)
To utilize the model, we can import it from sklearn and execute it.
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from sklearn.tree import DecisionTreeClassifier
model = DecisionTreeClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
The model’s predictions are now available. To assess its effectiveness, we can generate a confusion matrix, which is the same as we would do for a binary classification.
Confusion matrix interpretation for multiclass classification in Python differs from binary classifiers. The metrics module in Scikit-learn provides the requisite functions to accomplish multiclass classification as demonstrated in the following example.
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from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
fig, axe = plt.subplots(figsize=(8, 5))
cmp = ConfusionMatrixDisplay(confusion_matrix(y_test, y_pred),display_labels=["class_1", "class_2", "class_3"],)
cmp.plot(axe=axe)
plt.show();
An additional significant advantage of employing a decision tree is its ability to visualize the decision-making process. To achieve this, we need to install the pydotplus package and execute the “export graphviz” function, which will export the graph from the trained model.
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!pip install pydotplus
from sklearn import tree
from IPython.display import Image, display
import pydotplus
dot_data = tree.export_graphviz(model, out_file=None,filled=True, rounded=True)
graph = pydotplus.graph_from_dot_data(dot_data)
display(Image(data=graph.create_png()))
Iris and newsgroups are just two datasets that you can use to test this.
Evaluation metrics for multiclass classification
Earlier, we discussed how the confusion matrix can help tackle issues with multiple classes. Now, let’s examine some other metrics.
Metrics obtained from the confusion matrix are particularly crucial in binary classification. These metrics, such as accuracy, recall, and F1 score, can be universally applied to all categories. Additionally, accuracy, recall, and F1 score can be calculated independently for each category.
To gain a better understanding of the scenario, we trained a model using a simulated dataset that contained four classes.
Now, let’s take a look at the formulas for calculating the metrics for class A.
This analysis included a total of 93 samples, which comprised 59 Class A samples (samples that truly belong to Class A) along with 35 false positives (samples that were incorrectly labelled as Class A). To calculate accuracy, we determine the ratio of true positives to total positive results, which yields an accuracy of 0.627 for this sample set.
In summary, that is the mathematical rationale behind it. Although it is quite simple, it is an essential aspect of the process. The Scikit-learn library’s “classification_report()” function simplifies the process of obtaining these statistics for all categories.
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from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
It is easy to confirm that the precision value for Class 0 or Class A is the same as the estimated value with a small degree of approximation.
In this blog post, we have covered the fundamentals of multiclass classification and how it differs from binary classification. We have also discussed the various approaches for using machine learning models for this purpose. To demonstrate these concepts, we utilized a decision tree classifier. However, there are many other classifiers such as random forests, XGBoost, LightGBM, and more that can be used for multiclass classification.
Ensemble methods employ a mix of various decision trees to optimize performance. To handle multi-class classification tasks involving image datasets, deep learning models such as TensorFlow and PyTorch can be utilized. Similar principles are followed in constructing text categorization models as well.
