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By Vishakha Thakur
Machine learning, a process through which machines can learn and make predictions based on the provided data is one of the subsets of Artificial Intelligence.
Data is a crucial aspect in Machine learning as the response of the machine completely depends upon the data you feed it up with.
In one of our previous blogs, we shared all about machine learning and its types.
In this blog, our sole focus will remain mainly on key algorithms of machine learning.
● Linear regression
● Logistic regression
● Naive Bayes
● Decision tree
● Random forest
● Gradient boosting
● Support vector machine
Linear Regression
Linear regression is a supervised machine learning technique used to predict variables within a range, such as house prices. It is a statistical approach used to construct a linear relationship between an input variable X and an output variable Y.
Linear regression uses data points with known input and output values to identify the line that best matches those specific points. This line, often known as the “regression line,” represents a prediction model. Using this line, we can estimate or anticipate the output value Y based on an input value X.
Linear regression is primarily used for predictive modelling rather than classification.
Logistic Regression
Logistic regression, also known as ‘logit regression,’ is again a supervised learning technique being used for binary classification tasks. This type of algorithm mainly identifies whether an input belongs to a particular class or another.
Logistic regression determines whether an input can be classified into a single primary class. However, it is widely used to categorise outputs into two types: primary and non-primary classes. To accomplish this, logistic regression defines a threshold or boundary for binary categorization.
As a result, logistic regression is more commonly employed for binary categorization than predictive modelling. It allows us to classify incoming data based on the probability estimate and a predefined threshold. This makes logistic regression an effective tool for tasks like picture recognition, spam email detection, and medical diagnosis, which require categorising data into discrete classes.
Decision Tree
A decision tree is a supervised learning method used for categorization and predictive modelling. It resembles a flowchart, with a root node asking a specific query about the data. Based on the response, the data is routed along various branches to successive internal nodes, which pose additional questions and direct the data to subsequent branches.
Decision tree algorithms are widely used in machine learning because they can handle complex datasets with ease and simplicity. The algorithm’s structure simplifies understanding and comprehending the decision-making process. Decision trees allow us to classify or predict outcomes based on data features by asking questions and following the corresponding branches.
Decision trees are useful for a variety of machine learning applications due to their simplicity and interpretability, particularly when working with complicated datasets.
Random Forest
A random forest algorithm is a collection of decision trees used in classification and predictive modelling. Instead of depending on a single decision tree, a random forest aggregates predictions from numerous decision trees to get more accurate results.
In a random forest, a large number of decision tree algorithms (often hundreds or even thousands) are trained on diverse random samples from the training dataset. This sample procedure is known as “bagging.” Each decision tree is trained independently using its own random sample.
Once trained, the random forest sends the same data to all decision trees. Each tree makes a prediction, and the random forest totals the outcomes. The most common prediction from all decision trees is then chosen as the dataset’s final prediction.
Random forests solve a common “overfitting” problem with individual decision trees.
Support Vector Machine
A support vector machine is mainly used in classification as well as predictive modelling. SVM algorithms are dependably good in performance with little amounts of data. SVM algorithms work by establishing a decision boundary known as a “hyperplane.” This hyperplane functions as a line in two-dimensional space, separating two sets of labelled data.
SVM seeks the best possible decision boundary by increasing the margin between the two labelled data sets. It looks for the largest gap or spacing between the classes. Any new data point on either side of the decision boundary is classified using the labels from the training dataset.
It’s worth noting that hyperplanes can take on many shapes when displayed in three dimensions, allowing SVM to handle more complicated patterns and correlations in the data.
Gradient Boosting
Gradient boosting techniques use the ensemble method, which means they generate a succession of “weak” models that are iteratively refined to produce a strong prediction model. The iterative method eventually eliminates model errors, resulting in an ideal and accurate final model.
The algorithm begins with a simple, naive model that may make fundamental assumptions, such as categorising data depending on whether it is above or below the mean.
In each iteration, the algorithm creates a new model with the goal of fixing earlier models’ faults. It identifies patterns or linkages that earlier models failed to capture and combines them into the new model.
Gradient boosting is useful in dealing with complicated problems and vast datasets. It can detect subtle patterns and connections that a single model could overlook. Gradient boosting creates a strong predictive model by combining predictions from numerous models.
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