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…..
Tag: Sklearn
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….
Gradient Boosting (XGBoost, LightGBM, CatBoost)
Gradient Boosting (XGBoost, LightGBM, CatBoost) in Machine Learning 1. Introduction to Gradient Boosting Gradient Boosting is a powerful ensemble learning technique used in machine learning for classification and regression tasks…..
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….
Support Vector Machines (SVM)
Support Vector Machines (SVM) in Machine Learning 1. Introduction to Support Vector Machines (SVM) Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression problems. SVM….
Logistic Regression
Logistic Regression in Machine Learning 1. Introduction to Logistic Regression Logistic Regression is a Supervised Learning algorithm used for classification problems. Unlike Linear Regression, which predicts continuous values, Logistic Regression….
Polynomial Regression
Polynomial Regression in Machine Learning 1. Introduction to Polynomial Regression Polynomial Regression is an extension of Linear Regression that models the relationship between the independent variable (X) and the dependent….
Train-Test Split and Cross-Validation
Train-Test Split and Cross-Validation in Machine Learning In machine learning, evaluating the performance of a model is crucial to ensure it generalizes well to unseen data. Two widely used techniques….
Feature Selection Techniques
Feature Selection Techniques: A Comprehensive Guide Introduction Feature selection is a crucial step in machine learning that involves selecting the most relevant features (variables) for building an efficient and accurate….