Machine Learning

Grid Search practically Grid Search¶Grid search is a technique used in machine learning to systematically search for the best combination of hyperparameters for a given model. Hyperparameters are settings that are not learned from the data but are set by the user before training the model. In grid search, you …

grid search notes Here’s a simple explanation of Grid Search with steps for finding the best settings (hyperparameters) for a machine learning model: What is Grid Search?¶Grid Search is a process used to automatically test different combinations of hyperparameters to find the best one for your machine learning model. Instead …

F1-Score The F1 Score is a metric used to evaluate the performance of a classification model by combining both precision and recall into a single score. It provides a balance between precision and recall, especially when there is an uneven class distribution or when both false positives and false negatives …

Precision Score Precision Score is a metric used in classification tasks to measure how many of the positive predictions made by a model are actually correct. In simpler terms, it answers the question: Out of all the instances the model predicted as positive, how many were truly positive? The formula …

Recall Score The Recall Score (also known as Sensitivity or True Positive Rate) measures the ability of a classification model to correctly identify all relevant (positive) instances. In other words, it answers the question: Out of all the actual positive cases, how many did the model correctly predict? The formula …

Accuracy, Precision, Recall, and F1-Score Accuracy, precision, recall, and F1-score are commonly used performance metrics to evaluate the effectiveness of a classification model. These metrics provide insights into different aspects of the model’s performance in predicting class labels. Here’s a brief explanation of each metric: 1. Accuracy: Accuracy measures the …

Data preprocessingg What is Data Preprocessing?¶Data preprocessing is the process of preparing raw data for analysis or modeling. In machine learning, data usually needs to be cleaned and transformed into a suitable format before feeding it into algorithms. The main goal is to improve the quality of the data so …

1 K-Means Clustering Introduction to K-means Clustering¶K-means clustering is a type of unsupervised learning, which is used when you have unlabeled data (i.e., data without defined categories or groups). The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable …

2 – Hierarchical Clustering Hierarchical Clustering Analysis¶Clustering is the most common form of unsupervised learning, a type of machine learning algorithm used to draw inferences from unlabeled data. Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is …

Support Vector What is SVC? The Support Vector Classifier (SVC) is a method used in machine learning to classify data into different groups. It helps to find the best line (or boundary) that separates different classes of data. How SVC Works¶ Hyperplane: Imagine a line in 2D or a flat …