A Gaussian mixture model (GMM) is a statistical framework that assumes the underlying data were generated by combining several Gaussian distributions. This probabilistic model determines the probability density function of the data. The versatility of GMM is its main advantage. GMM can be used to model different data types and distributions. It can deal with data that has several peaks or modes, non-spherical clusters, and various modes. The GMM is robust to outliers and can be used for both density estimation and clustering applications. Picture segmentation and anomaly detection can both benefit from it. Time series information can be utilized ... Read More

A logistic model is a statistical framework for predicting the probability of an occurrence. These models are commonly used in industries including banking, healthcare, and marketing to assist with important business decisions. These models must be precise and reliable since the results reached from them can greatly affect how a project or business will end. It is essential to assess the model's quality to ensure that the predictions offered by a logistic model are trustworthy. Numerous metrics and techniques can be employed to determine a logistic model's accuracy and dependability. By properly analyzing a logistic model, businesses and academics can ... Read More

In several study fields, such as statistics, epidemiology, and machine learning, missing data is a major problem. Numerous factors, such as survey nonresponse, measurement problems, or incorrect data entry, might cause it. While imputation and maximum likelihood estimation are alternate approaches for handling missing data, they could introduce bias into the study. Selection bias, in particular, can be made worse by poor data management. This blog post will discuss the idea of selection bias, how missing data can introduce bias, and strategies for dealing with missing data that can minimize selection bias's impact. What is selection bias? Selection bias is ... Read More

Introduction In machine learning, linear regression is one of the best algorithms used for linear types of data and it returns very accurate predictions the same. Although after training a model with any algorithm it is necessary to check the performance of the algorithm to get an idea about how the model is behaving and what things are needed to improve the model. In this article, we will discuss the various evaluation metrics and the best metric to evaluate the linear regression algorithm. Why Find the Best Evaluation Metrics? There are many evaluation metrics available for regression type of algorithm ... Read More

Introduction Anomalies are values or data observations that are very different from the other observations in the existing datasets., Detecting and processing the anomalies become essential while building a machine learning model, as the quality of the data that is to be passed to the model should be fair enough to rely on. It is believed that high-quality datasets can give accurate and reliable information and result son even very poor-performing algorithms, and if the quality of the dataset is itself very poor, then there is very less probability of achieving a high-performing model. This article will discuss the outliers, ... Read More

Introduction Model validation is a technique where we try to validate the model that has been built by gathering, preprocessing, and feeding appropriate data to the machine learning algorithms. We can not directly feed the data to the model, train it and deploy it. It is essential to validate the performance or results of a model to check whether a model is performing as per our expectations or not. There are multiple model validation techniques that are used to evaluate and validate the model according to the different types of model and their behaviors. In this article, we will discuss ... Read More

Introduction Maximum likelihood is an approach commonly used for such density estimation problems, in which a likelihood function is defined to get the probabilities of the distributed data. It is imperative to study and understand the concept of maximum likelihood as it is one of the primary and core concepts essential for learning other advanced machine learning and deep learning techniques and algorithms. In this article, we will discuss the likelihood function, the core idea behind that, and how it works with code examples. This will help one to understand the concept better and apply the same when needed. Let ... Read More

Introduction Hyperparameter tuning in machine learning is a technique where we tune or change the default parameters of the existing model or algorithm to achieve higher accuracies and better performance. Sometimes when we use the default parameters of the algorithms, it does not suit the existing data as the data can vary according to the problem statement. In that case, the hyperparameter tuning becomes an essential part of the model building to enhance the model's performance. This article will discuss the algorithm's hyperparameter tuning, advantages, and other related things. This will help one understand the concept of hyperparameter tuning and ... Read More

Introduction Linear regression is one of the most used and simplest algorithms in machine learning, which helps predict linear data in almost all kinds of problem statements. Although linear regression is a parametric machine learning algorithm, the algorithm assumes certain assumptions for the data to make predictions faster and easier. Homoscadastocoty is also one of the core assumptions of linear regression, which is assumed to be satisfied while applying linear regression on the respected dataset. In this article, we will discuss the homoscedasticity assumption of linear regression, its core idea, its importance, and some other important stuff related to the ... Read More

The purpose of MLOps, is to standardize and streamline the continuous delivery of high performing models in production by combining ML systems development (dev) with ML systems deployment (ops). It aims to accelerate the process of putting machine learning models into operation, followed by their upkeep and monitoring. An ML Model must go through a number of phases before it is ready for production. These procedures guarantee that your model can appropriately scale for a wide user base. You'll run into that MLOps workflow. Why MLOps? Data ingestion, data preparation, model training, model tuning, model deployment, model monitoring, explainability, and ... Read More