Ensemble models in machine learning combine the output of multiple models to build a better performing model as compared to the individual models. The individual models whose output are used for the process of ensemble modeling are called baseline models or base estimators. The objective behind this approach of ensemble learning is to alleviate the […]
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Overview Birds are an incredibly diverse group of animals found all over the Earth. With over 10,000 known species, they come in all shapes, sizes, and colours, and they play a vital role in maintaining the delicate balance of ecosystems around the world. One of the most important roles that birds play is that of
Recognising Songs of Endangered Bird Species from Kerala using Deep Learning Techniques Read More »
Decision tree is a non-parametric supervised machine learning algorithm which makes use of a tree like structure to make decisions. Decision tree algorithms can be used for both classification and regression though it is mostly meant for classification tasks. Decision trees consist of three types of nodes such as root node, leaf nodes and decision
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This is another addition to the series of machine learning (ML) interview questions and answers that have been published on this website. What are Sigmoid function and softmax functions? Explain the difference Sigmoid and softmax functions are functions used for the classification tasks in machine learning and deep learning. Both Sigmoid function and softmax function
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Emotional intelligence can be understood as an ability to know our own emotions and being able to handle it in the best ways. Studies revealed intelligent quotients contribute 20 % of success in our life whereas 80% comes from our emotional intelligence. Emotional intelligence helps people to stay calm and hopeful when a difficult situation
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Logistic regression is an analytical technique in supervised machine learning for performing classification tasks. It is a classification algorithm based on probabilistic approach. Logistic regression approach can be used to make predictions on a categorical dependent variable. The algorithm extracts inferences from a given set of independent input variables and these inferences are used to
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Overview Materials science is a field of study that involves the research and discovery of new materials and its functional properties for applications in electronic storage devices, biomedical instrumentation, telecommunication equipment etc. The applications of material science spans from designing of products by exploiting its material properties to the manufacture of products which can contribute
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The K-nearest neighbor (KNN) is a supervised machine learning algorithm. KNN is used mostly to classify data points although it can perform regression as well. The K-Nearest Neighbors Algorithm classify new data points to a particular category based on its similarity with the other data points in that category. The KNN algorithms grab the freedom
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This article discusses a couple of machine learning (ML) interview questions asked frequently in data science related job roles. When should we prefer ridge regression over lasso? Explain with reason Ridge regression is a regression in which we use L2 regularization to give a penalty to the loss function to reduce loss in the optimization
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Introduction Hyperparameters are the parameters that are explicitly defined by the user for the machine learning model. This helps the learning process of the machine learning algorithm. The prefix “hyper” in hyperparameter means top-level, indicating these are the controlling parameters to ensure model performance. The user selects the value of the hyperparameter before the training
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