Tag: Feature Scaling
Running ML on raw, unprocessed data
Running machine learning (ML) on raw, unprocessed data is a critical yet intricate process that forms the backbone of any successful ML project. This comprehensive guide delves into each step….
Ignoring community modules’ security risks
Ignoring Community Modules’ Security Risks: Understanding the Importance of Secure IaC Practices Introduction Infrastructure as Code (IaC) is one of the cornerstones of modern DevOps practices, enabling teams to automate….
Running conflicting IaC deployments
Running Conflicting IaC Deployments: Understanding the Challenges and Best Practices Introduction Infrastructure as Code (IaC) has become the foundation for modern DevOps practices, allowing teams to define, provision, and manage….
Data leakage due to incorrect train-test split
Data leakage occurs when information from outside the training dataset is used to create the model, leading to overly optimistic performance during training but poor generalization on new data. One….
TypeError: expected sequence, got ndarray
The error “TypeError: expected sequence, got ndarray” occurs when a function expects a list, tuple, or other sequence-like data structure but receives a NumPy ndarray. Possible Causes and Fixes: 1…..
Not normalizing data before training ML models
Data normalization is a crucial preprocessing step in machine learning. If skipped, it can negatively affect model performance, particularly for algorithms that rely on feature scaling. In this guide, I….
Feature Engineering in Python
Feature engineering is one of the most crucial steps in the data preprocessing pipeline. It involves creating new features or modifying existing ones to improve the performance of machine learning….
K-Means Clustering
K-Means Clustering: A Comprehensive Guide 1. Introduction to K-Means Clustering K-Means Clustering is an unsupervised machine learning algorithm used for grouping similar data points into clusters. It aims to partition….
k-Nearest Neighbors (k-NN)
k-Nearest Neighbors (k-NN) Algorithm in Machine Learning 1. Introduction to k-Nearest Neighbors (k-NN) k-Nearest Neighbors (k-NN) is a supervised learning algorithm used for classification and regression tasks. It is a….