Author: Tayyaba Syed
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Centroid-Based Clustering
Centroid-based clustering is a class of machine learning algorithms that aims to partition a dataset into groups or clusters based on the proximity of data points to the centroid of each cluster. The centroid of a cluster is the arithmetic mean of all the data points in that cluster and serves as a representative point…
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Clustering Algorithms
Clustering methods are one of the most useful unsupervised ML methods. These methods used to find similarity as well as relationship patterns among data samples and then cluster those samples into groups having similarity based on features. Clustering is important because it determines the intrinsic grouping among the present unlabeled data. They basically make some…
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Stochastic Gradient Descent
Gradient Descent is a popular optimization algorithm that is used to minimize the cost function of a machine learning model. It works by iteratively adjusting the model parameters to minimize the difference between the predicted output and the actual output. The algorithm works by calculating the gradient of the cost function with respect to the…
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Confusion Matrix
It is the easiest way to measure the performance of a classification problem where the output can be of two or more type of classes. A confusion matrix is nothing but a table with two dimensions viz. “Actual” and “Predicted” and furthermore, both the dimensions have “True Positives (TP)”, “True Negatives (TN)”, “False Positives (FP)”,…
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Random Forest
Random Forest is a machine learning algorithm that uses an ensemble of decision trees to make predictions. The algorithm was first introduced by Leo Breiman in 2001. The key idea behind the algorithm is to create a large number of decision trees, each of which is trained on a different subset of the data. The…
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Support Vector Machine
Support vector machines (SVMs) are powerful yet flexible supervised machine learning algorithm which is used for both classification and regression. But generally, they are used in classification problems. In 1960s, SVMs were first introduced but later they got refined in 1990 also. SVMs have their unique way of implementation as compared to other machine learning…
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Decision Trees Algorithm
The Decision Tree algorithm is a hierarchical tree-based algorithm that is used to classify or predict outcomes based on a set of rules. It works by splitting the data into subsets based on the values of the input features. The algorithm recursively splits the data until it reaches a point where the data in each…
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Naive Bayes Algorithm
The Naive Bayes algorithm is a classification algorithm based on Bayes’ theorem. The algorithm assumes that the features are independent of each other, which is why it is called “naive.” It calculates the probability of a sample belonging to a particular class based on the probabilities of its features. For example, a phone may be…
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K-Nearest Neighbors (KNN)
KNN is a supervised learning algorithm that can be used for both classification and regression problems. The main idea behind KNN is to find the k-nearest data points to a given test data point and use these nearest neighbors to make a prediction. The value of k is a hyperparameter that needs to be tuned,…
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Logistic Regression
Logistic regression is a popular algorithm used for binary classification problems, where the target variable is categorical with two classes. It models the probability of the target variable given the input features and predicts the class with the highest probability. Logistic regression is a type of generalized linear model, where the target variable follows a…