Imagine you’re a real estate analyst tasked with predicting house prices based on factors like location, number of bedrooms, and house size. Accurately forecasting prices would require considering multiple variables simultaneously. Multiple Linear Regression (MLR) is a foundational tool in machine learning and statistics that allows us to do just that. It helps us understand ...
Machine Learning, a branch of Artificial Intelligence, enables systems to learn from data and make decisions without explicit programming. One of the foundational models in Machine Learning is the Artificial Neural Network (ANN), inspired by the structure of the human brain. A basic type of ANN is the Perceptron, which has a single layer and ...
Ridge Regression is a regularization technique used to reduce overfitting by imposing a penalty on the size of coefficients in a linear regression model. While standard linear regression can provide accurate predictions when there are minimal correlations among features, its performance declines when the dataset experiences multicollinearity (i.e., high correlations among independent variables). This makes ...
Machine learning involves building models that learn from data to make predictions or decisions. A hypothesis plays a crucial role in this process by serving as a candidate solution or function that maps input data to desired outputs. Essentially, a hypothesis is an assumption made by the learning algorithm about the relationship between features (input ...
In machine learning, evaluation metrics are essential to assess the effectiveness of models. Among these metrics, the F1 Score plays a crucial role, especially in classification tasks. It provides a balanced measure by considering both Precision and Recall, offering insights into a model’s overall accuracy in predicting the positive class. The F1 Score is particularly ...
Machine learning models rely on two types of configurations: parameters learned during training and hyperparameters that need to be manually set. Hyperparameters, such as learning rate in neural networks or C value in Support Vector Machines (SVMs), directly impact how well a model performs. Setting them incorrectly can result in underfitting or overfitting, making it ...
Regularization is a critical technique in machine learning used to improve the performance of models by reducing overfitting. Overfitting occurs when a model learns too much from the training data, capturing noise and irrelevant patterns that hinder its ability to generalize to new data. Regularization introduces a penalty term to the loss function, discouraging the ...
In machine learning, accuracy is a crucial performance metric used to evaluate how well a model predicts labels for unseen data. It measures the proportion of correct predictions to the total number of predictions. However, accuracy alone can be misleading in certain scenarios, such as with imbalanced datasets. For instance, a model predicting 99% of ...
Clustering is a fundamental task in machine learning, involving the grouping of similar data points. Density-based clustering methods, like DBSCAN (Density-Based Spatial Clustering of Applications with Noise), are highly effective for identifying clusters in noisy datasets. Unlike centroid-based methods, DBSCAN forms clusters based on data point density, making it suitable for datasets with arbitrary shapes. ...
Polynomial regression is an essential extension of linear regression used to model non-linear relationships in data. In many real-world scenarios, the relationship between variables isn’t linear, making polynomial regression a suitable alternative for achieving better predictive accuracy. This technique allows machine learning models to capture curved patterns in data by fitting polynomial equations of higher ...
Team Applied AI
Abhimanyu Saxena
