Machine Learning 101: Must Know Classification Models

The best classification algorithm for a particular problem depends on several factors, such as the size and type of the data, the number of classes, and the desired accuracy. Some of the most commonly used classification algorithms include:

• Logistic regression
• Decision trees
• Random forests
• Support vector machines (SVMs)
• K-nearest neighbors (KNN)
• Naive Bayes
• Neural networks

Each of these algorithms has its own strengths and weaknesses, and which one is best for a particular problem will depend on the specific characteristics of the data and the goals of the classification task. Some of the key factors to consider when choosing a classification algorithm include:

• The number of classes: For problems with a large number of classes, algorithms such as random forests and neural networks tend to perform well. For problems with only a few classes, simpler algorithms like logistic regression and decision trees may be more appropriate.
• The size of the dataset: For very large datasets, algorithms that scale well with the number of examples, such as random forests and SVMs, tend to be more effective. For smaller datasets, algorithms that can handle a larger number of features, such as KNN and decision trees, may be more appropriate.
• The type of data: For datasets with a large number of continuous features, algorithms such as SVMs and neural networks tend to perform well. For datasets with a large number of categorical features, algorithms such as decision trees and random forests may be more effective.

In this article, we’ll go through an overview of these must know classification models.

LOGISTIC REGRESSION

Logistic regression is a popular machine learning algorithm used for binary classification. It is a supervised learning algorithm, which means that it is trained on labeled data, and can be used to predict the class of new, unseen data.

Logistic regression is a popular machine learning algorithm used for binary classification. It is a supervised learning algorithm, which means that it is trained on labeled data, and can be used to predict the class of new, unseen data.

Logistic regression works by finding the relationship between a dependent variable and one or more independent variables. The dependent variable is the variable that we are trying to predict, and is typically binary (i.e., it can take on only two values, such as “positive” or “negative”). The independent variables are the variables that we use to predict the value of the dependent variable.

Logistic regression is a linear model, which means that it assumes that the relationship between the dependent and independent variables is linear. This means that the predicted probability of the dependent variable can be expressed as a linear combination of the independent variables.

However, there are ways to use logistic regression for non-linear relationships. One common approach is to use non-linear transformations of the independent variables, such as polynomial or spline transformations, to create new, derived variables that have a linear relationship with the dependent variable.

For example, if you have an independent variable X and a dependent variable Y, and you believe that the relationship between X and Y is non-linear, you could create a new variable X^2 (i.e., the square of X) and use logistic regression to model the relationship between Y and X^2. This would allow you to capture the non-linear relationship between X and Y using a linear model.

Another approach is to use a generalized linear model (GLM), which is a type of regression model that can be used for dependent variables that are not normally distributed. GLMs can be used to model non-linear relationships, and they can be extended to handle binary classification tasks (i.e., tasks where the dependent variable can take on only two values) using a logit link function.

Overall, while logistic regression is not designed for non-linear relationships, there are ways to adapt it for use with non-linear data. However, other machine learning algorithms, such as decision trees or neural networks, may be better suited for modeling non-linear relationships.

One of the key strengths of logistic regression is that it is very efficient, both in terms of the amount of training data it requires and the amount of time it takes to train the model. It is also easy to interpret, which makes it a popular choice for many applications.

Logistic regression has some limitations. It is only appropriate for binary classification tasks, and it assumes that the relationship between the dependent and independent variables is linear, which may not always be the case. In addition, it is sensitive to outliers and can be adversely affected by correlated predictors.

Overall, logistic regression is a powerful and widely-used machine learning algorithm that can be applied to a wide range of binary classification tasks.

DECISION TREES

Decision trees are a popular machine learning algorithm used for both classification and regression tasks. They are a type of supervised learning algorithm, which means that they are trained on labeled data and can be used to make predictions on new, unseen data.

Decision trees are called “trees” because they have a branching structure, with a series of decisions (or “nodes”) leading to a final prediction (or “leaf”). Each decision is based on the value of one or more input features, and the resulting prediction is made by following the path through the tree that corresponds to the input feature values.

One of the key strengths of decision trees is that they are easy to interpret and understand.

Because the decisions and predictions are made based on the input feature values, it is easy to see how a decision tree arrives at a particular prediction. This makes decision trees a popular choice for many applications, such as fraud detection and customer segmentation.

Like all models, decision trees have some limitations. They are prone to overfitting, which means that they can become too complex and fit the training data too closely, resulting in poor performance on new, unseen data. They are also not well-suited for datasets with a large number of continuous features, as it is difficult to find suitable decision boundaries for continuous data.

RANDOM FOREST

Random forests are a popular ensemble learning algorithm used for both classification and regression tasks. They are a type of supervised learning algorithm, which means that they are trained on labeled data and can be used to make predictions on new, unseen data.

Random forests are called “forests” because they are made up of a large number of decision trees. Each decision tree is trained on a different subset of the data, and the final prediction is made by combining the predictions of all of the individual trees. This process of training multiple models and combining their predictions is known as “ensemble learning”.

One of the key strengths of random forests is that they are very effective at reducing overfitting, which is a common problem with decision trees. Because each decision tree is trained on a different subset of the data, the individual trees are less likely to overfit the training data, and the final predictions are more accurate. This makes random forests a popular choice for many applications, such as credit scoring and medical diagnosis.

Random forests can be computationally expensive to train, especially for large datasets, and they may not be as interpretable as individual decision trees. They are also not well-suited for very high-dimensional datasets, as the number of trees in the forest can grow exponentially with the number of dimensions.

SUPPORT VECTOR MACHINES (SVM)

Support vector machines (SVMs) are a popular machine learning algorithm used for both classification and regression tasks. They are a type of supervised learning algorithm, which means that they are trained on labeled data and can be used to make predictions on new, unseen data.

SVMs are called “machines” because they are based on the mathematical concept of a “support vector”, which is a line (or hyperplane) that separates different classes of data. During training, an SVM finds the line (or hyperplane) that maximizes the margin between the different classes of data. The resulting line (or hyperplane) is called the “decision boundary”.

One of the key strengths of SVMs is that they can handle very high-dimensional datasets, which is a common problem in many machine learning applications. This is because SVMs find the decision boundary by optimizing a mathematical function, rather than searching for the best split in the data, as is done in decision trees. This makes SVMs a popular choice for many applications, such as text classification and image recognition.

SVMs can be difficult to interpret, as the decision boundary is determined by a mathematical function, rather than by the input feature values. They are also sensitive to the scale of the input features, and may require preprocessing to ensure that the data is properly normalized.

Overall, SVMs are a powerful and widely-used machine learning algorithm that can be applied to many different types of problems.

K-NEAREST NEIGHBORS (KNN)

K-nearest neighbors (KNN) is a popular machine learning algorithm used for both classification and regression tasks. It is a type of supervised learning algorithm, which means that it is trained on labeled data and can be used to make predictions on new, unseen data.

KNN is called “nearest neighbors” because it makes predictions based on the “nearest” training examples in the feature space. Given a new, unseen example, KNN finds the k training examples that are closest to the new example in the feature space, and then uses the labels of those k training examples to make a prediction.

One of the key strengths of KNN is that it is simple and easy to implement. Because it makes predictions based on the training examples, there is no need to train a complex model, as is done in many other machine learning algorithms. This makes KNN a popular choice for many applications, such as recommendation systems and anomaly detection.

KNN can be computationally expensive to find the nearest neighbors, especially for large datasets, and it may not be effective for high-dimensional datasets, as the curse of dimensionality can make it difficult to find meaningful nearest neighbors. It is also sensitive to the choice of the k parameter, which specifies the number of nearest neighbors to use for making predictions.

NAIVE BAYES

Naive Bayes is a popular machine learning algorithm used for both classification and regression tasks. It is a type of supervised learning algorithm, which means that it is trained on labeled data and can be used to make predictions on new, unseen data.

Naive Bayes is called “naive” because it makes a strong assumption about the independence of the input features. Specifically, it assumes that the value of each input feature is independent of the values of all other input features, given the class label. This assumption is often not true in real-world datasets, but despite this, Naive Bayes can still perform well in many applications.

One of the key strengths of Naive Bayes is that it is simple and efficient to implement. Because it makes predictions based on probabilities, rather than a complex mathematical model, it can be trained and used very quickly. This makes Naive Bayes a popular choice for many applications, such as spam filtering and text classification.

Naive Bayes assumes that the input features are independent, so it may not be effective for datasets where the features are highly correlated. It is also sensitive to the presence of irrelevant features, which can negatively impact the performance of the model.

NEURAL NETWORKS

Neural networks are a popular machine learning algorithm used for both classification and regression tasks. They are a type of supervised learning algorithm, which means that they are trained on labeled data and can be used to make predictions on new, unseen data.

Neural networks are called “networks” because they are made up of multiple interconnected “neurons” that process and transmit information. Each neuron receives input from other neurons, processes that input using a set of weights, and then transmits the output to other neurons. This process is repeated multiple times, resulting in a complex network of interconnected neurons that can learn to make predictions based on the input data.

One of the key strengths of neural networks is that they can learn complex, non-linear relationships between the input and output variables. This is because they are able to learn multiple layers of representations, each of which captures a different aspect of the data. This makes neural networks a popular choice for many applications, such as natural language processing and computer vision.

However, neural networks can be difficult to train, especially for large and complex datasets, and they can be sensitive to the choice of hyperparameters, such as the learning rate and the number of hidden layers. They are also not well-suited for tasks that require interpretability, as the internal workings of the network are difficult to understand.

Overall, choosing the best classification algorithm for a particular problem requires a thorough understanding of the data and the goals of the classification task. Experienced data scientists and machine learning practitioners can use their knowledge and experience to choose the most appropriate algorithm for a given problem.

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