Top 5 Classification Algorithms for Data Scientists

Imagine how annoying it would be if there was no spam folder to separate all those spam emails from the genuine ones! What if you had to manually filter and classify your mail inbox each day? Such a nightmare! Thanks to data science and machine learning, we have classification algorithms to do these tasks for us in our day-to-day lives.

Machine learning relies heavily on classification since it teaches computer systems how to classify data according to specific criteria, such as predefined attributes. With the growing use of big data for decision-making across businesses, classification algorithms have become an essential tool these days. Data scientists and researchers can better understand data and identify trends with the help of classification algorithms. Let us look at the top five classification algorithms that are highly popular among data scientists.

1. Decision Tree

A decision tree is a commonly-used supervised machine learning approach that involves segmenting a dataset according to specific feature variables. Internal nodes in a tree-structured classifier stand for a dataset's features, branches for the decision-making process, and each leaf node for the classification output. Common application areas of decision tree algorithms include financial analysis to identify user satisfaction levels with a product or service, biomedical engineering (for identifying features to be employed in implanted devices), and others.

2. Naive Bayes Classifier

The Nave Bayes algorithm, which falls under the domain of supervised learning algorithms, is a simple and effective classification technique that supports the development of efficient machine learning models capable of making accurate predictions. The algorithm, also referred to as a probabilistic classifier, makes predictions based on the likelihood of an object. The key advantage of Naive Bayes is that it performs rather well even with minimal amounts of training data, in contrast to most machine learning algorithms that require massive amounts of training data.

3. Logistic Regression

This algorithm uses statistics to determine a binary result; either something produces a specific result or it does not. The categorical dependent variable can be predicted most accurately using logistic regression. This algorithm is used by data scientists when the prediction is categorical, such as "true" or "false," "yes” or "no," or a 0 or 1. A logistic regression technique can also be used to determine whether or not an email is spam. The logistic regression method returns the probability of a label using the sigmoid function. A probability output is produced by the sigmoid function and t he object is given the appropriate label by contrasting the probability with a predetermined threshold.

4. Support Vector Machines

Support Vector Machine (SVM) is a popular supervised machine learning technique for classification and regression purposes. When using an SVM algorithm, you represent each data element as a point across (x, y) coordinates in N-dimensional space, with each feature denoting the value of a specific ordinate (x or y). Then, classification is carried out by locating the hyperplane that distinguishes the two classes. SVM is used in emotional analysis in systems that monitor and enhance employee performance. Compared to other classification models, SVM is more popular because of its kernel function, which increases computing efficiency.

5. K-Nearest Neighbour

The K-Nearest Neighbor (KNN) algorithm is designed around the concept of identifying the near-relatives in a training dataset. The data points are categorized according to the class of the majority of the data points among the k neighbors. The KNN method makes use of fundamental mathematical distance formulas like the Manhattan distance and the Euclidean distance. These calculations are employed to specify various levels of proximity or distance similarity among the k-nearest data points. KNN is the most simple and effective algorithm to use since, unlike the other algorithms, it doesn't need various parameter settings or additional assumptions.

Now that you have a fair understanding of these popular classification algorithms, you must try implementing them in some real-world data science projects. After all, a little practice goes a long way!!

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