Supervised learning is a type of machine learning in which a labeled dataset is used and the algorithm is performed on this labeled dataset containing both input and output data. Labeled data is data that has some predefined tags such as name, type, or number. The algorithm learns to identify patterns between the data input and output and using this knowledge it predicts the output for new input data. Anything when followed in an order yields better results preventing us from forgetting any points. Therefore, even in supervised learning, it is better to follow a specific path. This path consists of four steps. Step 1: Preparing data mainly focuses on preparing the data for passing it through the machine learning algorithm or the model. Here data is cleaned and made free from missing values by either filling the missing values with NaN or some or the other value. Another lesser-used option is deleting rows with missing data but this method is usually avoided as it reduces efficiency. Step 2: The second step is choosing an algorithm. There are a lot of algorithms like decision trees, SVM, Naive Bayes, KNN, etc and after knowing which model does what and which model would be the best fit. Step 3: Fitting the model with the appropriate data comes next. After we choose what algorithm we should perform we need to fit the model into it with the help of related commands. Step 4: The last step is to choose a validation method to analyze and examine the accuracy of the model. Although regular checks need to be done so that the model remains up to date with the data. supervised learning can further be classified into various other learning algorithms. Algorithms include neural networks, naive Bayes, linear regression, logistic regression, SVM or support vector machines, k-nearest neighbor and random forest. The above-mentioned learning algorithms can be categorized into two groups namely classification and regression. Classification users an algorithm to accurately assign data into specific categories. It helps to recognize entities within a dataset and attempt to draw conclusions based on houses in today's should be labeled or defined. The classification includes algorithms like decision trees, k nearest neighbor, random forest, support vector machines and linear classifier. Regression on the other hand is used to understand the relationship between dependent and independent variables. Linear regression logistical regression and polynomial regression are the most frequently used technique. Overview of the different algorithms: Neural networks are primarily used for deep learning algorithms. It mimics the interconnectivity of the human brain. Each node is made up of inputs, weights and a threshold value along with an output. Naive Bayes is a classification approach that adopts its working from the Bayes theorem. This means that the presence of one certain feature does not impact the presence of another in the probability of giving an outcome and each predictor has an equal effect on the result. This algorithm is used mainly in text classification spam identification and recommendation systems. To find the relationship between a dependent and an independent variable we use linear regression. This helps us make predictions about future outcomes. Logistic regression on the other hand is used when dependent variables have binary output like yes and no or true and false. A support vector machine is mainly used to categorize input points on either side of a decision boundary or plane. Used for classification problems to construct hyperplane weather distance between two classes of data points is a maximum. K-nearest neighbors used mainly for classification system uses a non-parametric algorithm that classifies data based on proximity and association to other available data. It uses the Euclidean distance formula and since it takes less time it is preferred by data scientists. Random forest is a very flexible supervisor machine learning algorithm used for board regression as well as classification. These references to the collection of uncorrelated decision trees are later merged to make more accurate data predictions. Image and object detection, predictive analysis, customer sentiment analysis, and spam detection are among the many applications of supervised learning. This article provided us with an overview of supervised learning as to what it is, what are the steps, what are its applications, etc. To know about unsupervised learning, reinforcement learning and more, click on the following link.
Published on: May 14, 2023
#unsupervisedlearning, #unlabeleddata, #insights, #patterns, #applications, #predictions
Published on: May 14, 2023
#reinforcementlearning, #agent, #rewards, #punishments, #feedback, #qlearning, #deepreinforcementlearning, #application
#supervisedlearning, #labeleddata, #classification, #regression, #applications, #neuralnetworks, #randomforest, #fittingmodel, #preprocessing
Published: May 14, 2023
Author: Dipti Vatsa
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