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Articles on Trending Technologies
Technical articles with clear explanations and examples
What is Loss Function in Data Science
Introduction A loss function, often referred to as a cost function or an error function, is a metric used in data science to assess how well predictions made by a machine learning model match the actual values or goals in the training data. It quantifies the difference between real and predicted values and offers a single scalar number that exemplifies the model's effectiveness. Problems with Multi−Collinearity n is the number of data points in the dataset. y represents the true values of the target variable. ŷ represents the predicted values generated by the regression model. The choice of ...
Read MoreHow to Evaluate a Logistic Regression Model?
Introduction Logistic regression is a prominent statistical approach for predicting binary outcomes such as disease presence or absence or the success or failure of a marketing effort. While logistic regression may be an effective method for predicting outcomes, it is critical to assess the model's performance to verify that it is a good match for the data. There are various ways for assessing the performance of a logistic regression model, each with its own set of advantages and disadvantages. This article will go through the most popular methods for assessing logistic regression models, such as the confusion ...
Read MoreThe Right Cross-Validation Technique for Time Series Dataset
Introduction Whenever working with time series data, it is critical to employ a cross−validation approach that accounts for the data's temporal ordering. This is because time series data displays autocorrelation, which means that the values of the data points are connected with their prior values. As a result, unlike in many other machine learning applications, the data cannot be deemed independent and identically distributed (iid). The standard k−fold cross−validation technique, which splits the data into k−folds at random and trains the model on k−1 folds before testing it on the remaining fold, is inadequate for time series data. ...
Read MoreOne Hot Encoding and Label Encoding Explained
Introduction Categorical variables are extensively utilized in data analysis and machine learning. Many algorithms are incapable of directly processing these variables, and they must be encoded or translated into numerical data before they can be used. Hot encoding and label encoding are two popular methods for encoding categorical data. One hot encoding provides a binary vector for each category in a categorical variable, indicating whether that category exists or not. We will discuss the ideas of one hot encoding and label encoding, as well as their advantages and disadvantages, and present examples of when and how to ...
Read MoreWhy Ordinary Least Square (OLS) is a Bad Option to Work With?
Introduction Ordinary least squares is a well−liked and often used method for linear regression analysis (OLS). For data analysis and prediction, however, it is not always the best option. OLS has several limitations and presumptions that, if not properly addressed, might provide biased and false results. The drawbacks and restrictions of OLS will be covered in this article, along with some reasons why it might not be the ideal choice for all datasets and applications. We will also look at additional regression analysis approaches and methodologies that can get around OLS's drawbacks and deliver more accurate and trustworthy findings. ...
Read MoreMethods to Select Important Variables from a Dataset
Introduction Moment's big data period requires a dependable and effective approach to opting for important variables from datasets. With so numerous functions available, it can be delicate to identify which bone has the most impact on the target variable. opting for only the most important variables improves model performance, improves model interpretability, and reduces the threat of overfitting. This composition describes numerous ways to remove important variables from your dataset. We'll go through both basic statistical approaches like univariate feature selection and regularization, as well as more sophisticated techniques like PCA and feature importance ...
Read MoreBuilding a Fraud Detection Model for a Bank
Introduction Financial fraud has become an increasingly common problem for banks and financial organizations throughout the world as technology advances. Money laundering, identity theft, and credit card fraud can all result in major financial losses as well as damage to a bank's image. As a result, banks must take proactive steps to prevent and detect fraudulent activity. Building a fraud detection model is one such method that can assist identify fraudulent transactions and flag them for further examination. In this article, we will examine the steps involved in creating a fraud detection model for a bank, starting with ...
Read MoreHow to Take Input from User Separated by Space in Java?
Input and output are the vital components of all the programming languages. Same is the case with Java. User input is very crucial for creating dynamic and interactive applications. Usually the input is a single value but we can also take input from the user separated by space. This article deals with how to take input from the user separated by spaces in Java. Ways to Take Input From User Separated By Space in Java There are 2 ways by which we can take the input from the user separated by space. They are as follows- ...
Read MoreHow to Create a User-Defined Javap Tool?
At times, we need information related to a class file. In such a case, we can use the javap tool provided by the Java Development Kit (JDK). We can get more information related to the methods, constructors, and fields present in the class. The purpose of the javap tool is to disassemble one or more class files. It is also known as Java Class File Disassembler. Using the javap tool, we can get more information about the bytecode information about that particular class. The output may vary depending on the options used. Syntax The syntax of javap is ...
Read MoreHow to Create a TreeSet with a List in Java?
A TreeSet in Java stores unique elements in sorted order. It implements the SortedSet interface. The TreeSet interface internally uses a balanced tree called the Red-Black tree. A List in Java is a data structure that is used to store elements in the order in which they were added. We can create a TreeSet with a List in Java in many ways. This article deals with the ways in which a TreeSet can be created using a List in Java. Ways to Create a TreeSet with a List in Java There are 3 ways by which a TreeSet ...
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