Welcome to an overview of machine learning classifiers. Here, we’ll delve into several classification algorithms, including the popular k Nearest Neighbor (kNN), logistic regression, decision trees, random forests, SVMs, and Bayesian classifiers.
Our aim is to help you comprehend the inner workings of machine learning. We won’t be covering all algorithms, for example, Linear Discriminant Analysis or kMeans (used for unsupervised learning and clustering). However, you can find plenty of online tutorials to fill in those gaps.
Let’s jump right into our classifier exploration!
Exploring Various Classifiers
To start with, we’ll look at the various classifiers previously mentioned. We will also delve into activation functions, which play a pivotal role in deep neural networks.
You’ll learn their significance in neural networks and gain insight into the TensorFlow APIs for activation functions, along with their advantages. This understanding will lay a solid foundation for logistic regression, which employs the sigmoid function, frequently used in Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTMs).
Further on, we will dive into a code sample featuring Logistic Regression and the MNIST dataset. It’s worth noting that this section may require a basic understanding of hidden layers in a neural network. Therefore, do refer to relevant online resources for a smoother experience.
Breaking Down Classification
So, what exactly is classification? Imagine having a dataset filled with observations, where each observation belongs to a known class or category. Classification is the process of determining the class of a new, unseen datapoint. These classes could be anything – like spam or not spam in an email, or the ten digits in the MNIST dataset. Classification finds wide application in areas like credit approval, medical diagnosis, and targeted marketing.
Deciphering Classifiers
In the previous chapter, you learned that linear regression uses supervised learning with numerical data. The objective is to train a model to make numerical predictions like the stock price or the temperature of a system.
Classifiers, on the other hand, employ supervised learning with different classes of data. Their goal is to train a model that can make categorical predictions. Suppose you have a dataset where each row is a unique wine, and each column represents a specific wine feature (like tannin or acidity). Let’s assume we have five types of wine labeled A through E. Given a new row of data (a new wine), a classifier attempts to assign one of these labels to this new wine.
Popular Classifiers
The machine learning world is teeming with popular classifiers, such as linear classifiers, kNN, logistic regression, decision trees, random forests, SVMs, Bayesian classifiers, and deep learning’s CNNs.
Each classifier comes with its unique advantages and potential trade-offs between complexity and accuracy. For instance, Convolutional Neural Networks (CNNs) excel at image classification, marking them as powerful classifiers.
Binary vs. MultiClass Classification
Classification isn’t confined to two categories. While binary classifiers deal with two classes, multiclass classifiers can distinguish more than two classes.
Random forest and naïve Bayes classifiers support multiple classes, while SVMs and linear classifiers serve as binary classifiers. However, there are multi-class extensions for SVM. Techniques like One-versus-All (OvA) and One-versus-One (OvO) are often employed for multiclass classification, leveraging binary classifiers.
MultiLabel Classification
In a step beyond multiclass classification, we encounter multilabel classification. It involves assigning multiple labels to an instance from a dataset. If you’re curious to learn more about it, check out this insightful multilabel classification tutorial featuring Keras.
What Are Linear Classifiers?
In essence, a linear classifier partitions a dataset into two classes. In the context of linear classifiers, speed often takes center stage. They excel when dealing with sparse input vectors or when the number of dimensions is vast.
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