김태오

Supervised learning algorithm: Logistic regression is a supervised learning algorithm used for classification tasks, predicting the probability of an instance belonging to a specific class or category. Binary outcome: It is mainly used for binary classification problems, where the output can be either of the two distinct classes, typically denoted as 0 or 1, such as spam or not spam, default or ..

a type of machine learning algorithm inspired by the structure and function of the human brain. They consist of multiple layers of interconnected nodes, or neurons, that process and transmit information. Each neuron takes input data, performs a mathematical operation on it, and passes the result to the next layer of neurons. During training, the network adjusts the strength of connections betwee..

a mathematical function or a line that separates different classes or categories in a dataset. It is the boundary or the threshold where a particular observation is classified into one of the categories. The decision boundary is determined by the algorithm based on the input features of the data. The goal of a machine learning model is to find the decision boundary that correctly classifies the ..

Artificial Neuron: A perceptron is an artificial neuron or a simplified model of a biological neuron, used as a building block for artificial neural networks. Binary Classification: Perceptrons are primarily used for binary classification tasks, where the goal is to categorize input data into one of two possible classes or categories. Linear Model: It is a linear model that takes a weighted sum ..

Cross-Entropy (CE) loss: A cost function commonly used to evaluate the performance of classification models, particularly for probabilistic outputs. Calculation: CE loss measures the dissimilarity between the predicted probability distribution (y_pred) and the true probability distribution (y_true) for each class. Probabilistic Models: Particularly suitable for models that output probabilities, ..

MSE(Mean Squared Error) Error Metric: It is a commonly used error metric in regression problems. Calculation: MSE is calculated by taking the average of the squared differences between the predicted values and the true values. Objective: The main goal in minimizing MSE is to improve the model's accuracy by reducing the squared errors. Sensitivity: MSE is more sensitive to outliers, as it squares..