# Category Archives: Machine Learning

## Parametric vs Non-Parametric Models: Differences, Examples

Last updated: 11 Aug, 2024 When working with machine learning models, data scientists often come across a fundamental question: What sets parametric and non-parametric models apart? What are the key differences between these two different classes of models? What needs to be done when working on these models? This is also one of the most frequent questions asked in the interviews. Machine learning models can be parametric or non-parametric. Parametric models are those that require the specification of some parameters before they can be used to make predictions, while non-parametric models do not rely on any specific parameter settings and therefore often produce more accurate results. These two distinct approaches …

## How to know if Linear Regression Model is Appropriate?

If you want to build a model for predicting a numerical value and wondering whether the linear regression model is most appropriate or valid, then creating the regression scatter plot is the most efficient way. And, this works best if the data set is two or three-dimensional. If a dataset is two-dimensional, it’s simple enough to plot the data to determine its shape. You can plot three-dimensional data too. The objective is to find whether the data set is relatively linear. When the plot is created, the data points fall roughly along a straight line as shown below. Whether Linear Regression Appropriate for High-dimension Datasets? The challenge comes when we …

## Lasso Regression in Machine Learning: Python Example

Last updated: 10th Aug, 2024 Lasso regression, sometimes referred to as L1 regularization, is a technique in linear regression that incorporates regularization to curb overfitting and enhance the performance of machine learning models. It works by adding a penalty term to the cost function that encourages the model to select only the most important features and set the coefficients of less important features to zero. This makes Lasso regression a popular method for feature selection and high-dimensional data analysis. In this post, you will learn concepts, formulas, advantages, and limitations of Lasso regression along with Python Sklearn examples. The other two similar forms of regularized linear regression are Ridge regression and …

## Completion Model vs Chat Model: Python Examples

In this blog, we will learn about the concepts of completion and chat large language models (LLMs) with the help of Python examples. What’s the Completion Model in LLM? A completion model is a type of LLM that takes a text input and generates a text output, which is called a completion. In other words, a completion model is a type of LLM that generates text that continues from a given prompt or partial input. When provided with an initial piece of text, the model uses its trained knowledge to predict and generate the most likely subsequent text. A completion model can generate summaries, translations, stories, code, lyrics, etc depending on …

## Python Pickle Security Issues / Risk

Suppose your machine learning model is serialized as a Python pickle file and later loaded for making predictions. In that case, you need to be aware of security risks/issues associated with loading the Python Pickle file. Security Issue related to Python Pickle The Python pickle module is a powerful tool for serializing and deserializing Python object structures. However, its very power is also what makes it a potential security risk. When data is “pickled,” it is converted into a byte stream that can be written to a file or transmitted over a network. “Unpickling” this data reconstructs the original object in memory. The danger lies in the fact that unpickling …

## Pricing Analytics in Banking: Strategies, Examples

Last updated: 15th May, 2024 Have you ever wondered how your bank decides what to charge you for its services? Or, perhaps how do banks arrive at the pricing (fees, rates, and charges) associated with various banking products? If you’re a product manager, data analyst, or data scientist in the banking industry, you might be aware that these pricing decisions are far from arbitrary. Rather, these pricing decisions are made based on one or more frameworks while leveraging data analytics. They result from intricate pricing strategies, driven by an extensive array of data and sophisticated analytics. In this blog, we will learn about some popular pricing strategies banks execute to …

## Machine Learning Lifecycle: Data to Deployment Example

Last updated: 12th May 2024 In this blog, we get an overview of the machine learning lifecycle, from initial data handling to the deployment and iterative improvement of ML models. You might want to check out this book for greater insights into machine learning (ML) concepts – Machine Learning Interviews. The following is the diagram representing the machine learning lifecycle while showcasing three key stages such as preparing data, ML development, and ML deployment. These three stages are explained later in this blog. Stage A: Preparing Data Preparing data for training machine learning models involves collecting data, constructing data pipelines for preprocessing, and refining the data to prepare it for …

## Autoencoder vs Variational Autoencoder (VAE): Differences, Example

Last updated: 12th May, 2024 In the world of generative AI models, autoencoders (AE) and variational autoencoders (VAEs) have emerged as powerful unsupervised learning techniques for data representation, compression, and generation. While they share some similarities, these algorithms have unique properties and applications that distinguish them. This blog post aims to help machine learning / deep learning enthusiasts understand these two methods, their key differences, and how they can be utilized in various data-driven tasks. We will learn about autoencoders and VAEs, understanding their core components, working mechanisms, and common use cases. We will also try and understand their differences in terms of architecture, objectives, and outcomes. What are Autoencoders? …

## Feature Engineering in Machine Learning: Python Examples

Last updated: 3rd May, 2024 Have you ever wondered why some machine learning models perform exceptionally well while others don’t? Could the magic ingredient be something other than the algorithm itself? The answer is often “Yes,” and the magic ingredient is feature engineering. Good feature engineering can make or break a model. In this blog, we will demystify various techniques for feature engineering, including feature extraction, interaction features, encoding categorical variables, feature scaling, and feature selection. To demonstrate these methods, we’ll use a real-world dataset containing car sales data. This dataset includes a variety of features such as ‘Company Name’, ‘Model Name’, ‘Price’, ‘Model Year’, ‘Mileage’, and more. Through this …

## Feature Selection vs Feature Extraction: Machine Learning

Last updated: 2nd May, 2024 The success of machine learning models often depends on the quality of the features used to train them. This is where the concepts of feature extraction and feature selection come in. In this blog post, we’ll explore the difference between feature selection and feature extraction, two key techniques used as part of feature engineering in machine learning to optimize feature sets for better model performance. Both feature selection and feature extraction are used for dimensionality reduction which is key to reducing model complexity given that higher model complexity often results in overfitting. We’ll provide examples of how they can be applied in real-world scenarios. If …

## Model Selection by Evaluating Bias & Variance: Example

When working on a machine learning project, one of the key challenges faced by data scientists/machine learning engineers is to select the most appropriate model that generalizes well to unseen datasets. To achieve the best generalization on unseen data, the model’s bias and variance need to be balanced. In this post, we’ll explore how to visualize and interpret the trade-off between bias and variance using a residual error vs. model complexity plot. We’ll use a specific plot to guide our discussion. The following is the residual error vs model complexity plot that would need to be drawn for evaluating the model bias vs variance for model selection. We will learn …

## Bias-Variance Trade-off in Machine Learning: Examples

Last updated: 1st May, 2024 The bias-variance trade-off is a fundamental concept in machine learning that presents a challenging dilemma for data scientists. It relates to the problem of simultaneously minimizing two sources of residual error that prevent supervised learning algorithms from generalizing beyond their training data. These two sources of error are related to Bias and Variance. Bias-related errors refer to the error due to overly simplistic machine learning models. Variance-related errors refer to the error due to too much complexity in the models. In this post, you will learn about the concepts of bias & variance in the machine learning (ML) models. You will learn about the tradeoff between bias …

## Mean Squared Error vs Cross Entropy Loss Function

Last updated: 1st May, 2024 As a data scientist, understanding the nuances of various cost functions is critical for building high-performance machine learning models. Choosing the right cost function can significantly impact the performance of your model and determine how well it generalizes to unseen data. In this blog post, we will delve into two widely used cost functions: Mean Squared Error (MSE) and Cross Entropy Loss. By comparing their properties, applications, and trade-offs, we aim to provide you with a solid foundation for selecting the most suitable loss function for your specific problem. Cost functions play a pivotal role in training machine learning models as they quantify the difference …

## Cross Entropy Loss Explained with Python Examples

Last updated: 1st May, 2024 In this post, you will learn the concepts related to the cross-entropy loss function along with Python code examples and which machine learning algorithms use the cross-entropy loss function as an objective function for training the models. Cross-entropy loss represents a loss function for models that predict the probability value as output (probability distribution as output). Logistic regression is one such algorithm whose output is a probability distribution. You may want to check out the details on how cross-entropy loss is related to information theory and entropy concepts – Information theory & machine learning: Concepts What’s Cross-Entropy Loss? Cross-entropy loss, also known as negative log-likelihood …

## Logistic Regression in Machine Learning: Python Example

Last updated: 26th April, 2024 In this blog post, we will discuss the logistic regression machine learning algorithm with a python example. Logistic regression is a regression algorithm specifically designed to estimate the probability of an event occurring. For example, it can be used in the medical field to predict the likelihood of a patient developing a certain disease based on various health indicators, such as age, weight, and blood pressure. It is often used in machine learning applications. In this blog, we will learn about the logistic regression algorithm, and use python to implement the logistic regression model with IRIS dataset. What is Logistic Regression? The logistic regression algorithm …

## MSE vs RMSE vs MAE vs MAPE vs R-Squared: When to Use?

Last updated: 22nd April, 2024 As data scientists, we navigate a sea of metrics to evaluate the performance of our regression models. Understanding these metrics – Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-Squared – is crucial for robust model evaluation and selection. In this blog, we delve into the intricacies of these different metrics while learning them based on clear definitions, formulas, and guidance on when to use which of these metrics. Different Types of Regression Models Evaluation Metrics The following are different types of regression model evaluation metrics including MSE, RMSE, MAE, MAPE, R-squared, and Adjusted …

I found it very helpful. However the differences are not too understandable for me