We encounter various applications of machine learning like spam filtering of emails, YouTube video suggestions, surge pricing in Uber, etc in our daily lives. However, we don’t know exactly what machine learning techniques are being employed to facilitate these machine-learning applications. In this article, we will discuss various machine learning techniques that are used by software engineers to build machine-learning applications.
Machine Learning Techniques For Classification
Classification algorithms are supervised machine learning techniques that are used to categorize data based on a given training dataset. Classification algorithms are used in applications like speech recognition, spam detection, drug classification, identification of tumor cells, biometric identification, and others.
Following are the machine learning techniques for classification.
The Naive Bayes classifier is based on Bayes’ theorem. It works on a probabilistic approach and is considered one of the simplest machine learning techniques for classification. It requires a very small training dataset and is one of the fastest techniques used for classification.
- The Naive Bayes classifier assumes that one feature of the training dataset is completely independent of the other features.
- Although Naive Bayes classifiers are one of the fastest and easiest algorithms to implement, Naive Bayes Classifiers have also a disadvantage. Naive Bayes classifiers are more inaccurate compared to other classification techniques.
Naive Bayes Classifiers are mainly used in text classification applications like spam filtering, sentiment analysis, and document classification.
Decision trees are also supervised machine learning techniques used for classification. Decision Tree classifiers are structures like a Binary tree where each internal node works as a decision node and classifies the data based on an attribute. When we reach the leaf node of the decision tree, the data is classified into a particular class.
- Decision trees are one of the most used machine learning techniques for classification because they mimic human interpretation. Each decision node of the decision tree classifies the data based on whether a condition is true or not.
- Additionally, a decision tree can handle categorical as well as numeric data while classifying a data entry.
- Using decision trees also has a fair share of disadvantages. If the training dataset contains more attributes, it is possible that the decision tree can become very complex and cannot be generalized well.
- Also, the decision tree can become unstable. In such a case, a little variation in data will lead to a major change in the structure of the decision tree, which is not the desired phenomenon.
To overcome the shortcomings of decision tree algorithms, we use random forest classifiers. A random forest classifier consists of many decision trees. Each decision tree is trained on a subset of training data. After that, each decision tree is combined to create the random forest classifier. A large number of decision trees in the classifier make sure that we obtain high accuracy with over-fitting the classifier.
- One of the main advantages of a random forest classifier is that It can maintain accuracy even if a significant portion of the input data is missing. This is due to the fact that the decision trees in the random forest are trained on subsets of the input dataset. Hence, the decision trees work efficiently even if some attributes are missing from the input data.
- Random forest classifiers are also efficient in handling comparatively large datasets.
- The disadvantages of random forest classification techniques include the complexity of the algorithm. Due to the involved complexity, random forest classifiers are also difficult to implement.
- Another major disadvantage of the random forest classification technique is the slow real-time prediction. This is due to the presence of a number of decision trees. Hence, increased accuracy comes at a cost of increasing the time involved in classification.
K Nearest Neighbors Algorithm
K Nearest Neighbor (KNN) algorithm is one of the machine learning techniques that is used both for classification and regression. As the name suggests, the KNN algorithm works on the similarity of input data from its neighbors. It stores all the data points in the training dataset. When we feed new data to the algorithm, it puts the data into the class where its neighbors have almost similar features.
- KNN algorithm is a non-parametric machine learning technique. Hence, it doesn’t make any assumptions about the training dataset. While training, it just stores the training data points and their associated classes. Whenever we enter a test data, it classifies the test data into a class whose existing data points are nearest to the test data.
- The KNN algorithm is one of the simplest machine learning techniques to implement. With robustness to noisy training data, the KNN algorithm also works effectively when the training dataset is very large.
- The drawback of the K Nearest Neighbors algorithm includes the high computational cost as we need to compute the distance of each data point in the training set and the test data.
Support Vector Machines
Support Vector Machine(SVM) algorithm creates a maximum margin hyperplane that divides the data points into classes. Hence, the hyperplane works as the boundary for the classes. The extreme data points that are nearest to the maximum margin hyperplane are called support vectors. Hence, we term this machine learning technique a support vector machine.
- SVM algorithms are memory efficient because they only use a subset of the training data in the decision function. Additionally, SVM algorithms are also efficient in high-dimensional spaces.
- A major disadvantage of using a support vector machine for classification is that the model doesn’t provide probability estimates for classification. We need to calculate the probability of a data point belonging to a class using cross-validation.
Machine Learning Techniques for Clustering
Clustering Algorithms are unsupervised machine-learning techniques used to classify data into groups or clusters. We use clustering algorithms for tasks such as market segmentation, social network analysis, and anomaly detection. Following are some of the machine learning techniques used for clustering.
Partitioning Clustering (Used in K Means Clustering)
In partitioning clustering, the machine learning model divides the data into non-hierarchical groups. The machine learning model chooses K number of centroids and the dataset is clustered into k groups according to the distance from the centroid. The K-means clustering algorithm works using the partitioning clustering technique.
In K-Means clustering, we initially choose K data points that work as centroids. After that, the centroid and the data points in the associated cluster are adjusted. The adjustment is done to make sure that the distance of the data point from the centroid of the current cluster is minimum compared to other centroids.
Hierarchical Clustering (Used in Agglomerative Clustering)
In hierarchical clustering, we don’t need to specify the number of clusters. In this machine learning technique, the dataset is divided hierarchically to form tree-like structures called dendrograms. Agglomerative clustering is a prime example of hierarchical clustering.
In agglomerative clustering, we start from the individual data points and merge them based on their attributes until we find the required number of clusters. Finally, we depict the cluster hierarchy as a tree.
Density-Based Clustering ( Used in DBSCAN Algorithm)
Density-based clustering is a machine-learning technique that groups highly dense areas (data points with the highest similarity in attributes) into a single cluster. In this clustering approach, the machine learning model identifies the subsets of data with the highest similarity and converts each subset into a cluster.
The DBSCAN clustering algorithm is one of the most popular examples of density-based clustering algorithms. Here, DBSCAN is an acronym for Density-Based Spatial Clustering of Applications with Noise.
Machine Learning Techniques for Association Rule Mining
Association rule mining consists of machine learning techniques used in various applications such as market basket analysis, protein sequence identification, and loss-leader analysis. In association rule mining, we identify the dependency of one data point upon another.
For association rule mining, we use different algorithms such as the Apriori algorithm, FP-Growth algorithm, Eclat algorithm, and other versions of these algorithms.
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Machine Learning Techniques for Regression
Regression algorithms are a class of machine learning techniques used for predictive modeling. In a regression algorithm, the machine learning model establishes a relationship (function) between independent and dependent attributes of the training dataset. After that, the identified relation/function is used to predict the value for the dependent variable when we are provided with the independent variables.
Regression algorithms are used in tasks such as price prediction, prediction of user trends, and creating time series models for forecasting and visualization. The most common machine learning techniques for regression analysis are Linear Regression and Logistic Regression.
Linear regression can be used to model output for a set of independent variables when there is a high chance that dependent variables are linearly related to the independent variables.
When we use a single independent variable, the regression algorithm is called simple linear regression. When we have two or more independent variables, linear regression is said to be multiple linear regression.
We use logistic regression in cases when we have only two dependent variables. In such a case, the dependent variable can be considered categorical data and we can predict the probability of occurrence of a dependent variable when we are provided with the independent variables.
Due to its nature, logistic regression is also used in binary classification problems.
In this article, we have discussed different machine learning techniques that we use in daily life, knowingly or unknowingly. I hope you enjoyed reading this article.
Stay tuned for more informative articles. Happy Learning!