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The path of a current file is defined with the help of directory hierarchy; and it contains the backtracked path from the current file to the root directory this file is present in. For instance, consider a file “my_file” belongs to a directory “my_directory”, the path for this file is defined as given below ./my_directory/my_file The directory, sub-directory and the file are all separated using the “/” separator in the path. Therefore, to get current file's full path, you can use the os.path.abspath() function. If you want only the directory path, you can call os.path.dirname() method. Using os.path.abspath() Method ... 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
We can use JavaScript Math.random() method to generate random values. JavaScript provides us with different Array methods, such as from(), fill(), and push(), that can be used to create an array with random values. Arrays in JavaScript are a powerful feature that allows you to store a collection of values of different data types. They are widely used in many applications, and having an array with random values can be very useful in certain scenarios. An array of random values is a collection of randomly generated numbers stored in an array. The values in an array of random values can ... Read More
The intersection of two arrays in JavaScript can be done in different ways, such as using the Set, spread operator, filter() method, or the includes() method. Each method has its own advantages and disadvantages, and it is important to consider which method works best for the given task. The intersection of two arrays is helpful if you want to create a new array that contains only the elements found in both arrays. Let’s have an example − arr1 = [ 2, 5, 7, 9, 11, 13, 15, 17 ] arr2 = [ 1, 3, 5, 7, 11, 13, 16, 17 ... Read More
Arrays are one of the most commonly used data types in JavaScript. They are used to store collections of data and allow for efficient access and manipulation of data. Arrays can contain any type of data, including primitive values, objects, and even other arrays. The technique of creating an array of partial objects from an array is a valuable technique when dealing with complex data sets. Partial objects contain only a subset of the data from the original array, allowing us to focus on a particular set of data. This can be especially useful when dealing with large data sets, ... Read More