세종대학교 소프트웨어 특강 과제1

문서 내 토픽

1. Linear Regression

주어진 데이터에 대해 가장 잘 맞는 선형 회귀 모델을 찾았습니다. Gradient Descent 알고리즘을 사용하여 모델의 최적 매개변수를 구했으며, 이를 통해 입력 x=15에 대한 y 값을 예측할 수 있었습니다. 또한 회귀선을 데이터 포인트와 함께 시각화하였습니다.
2. Logistic Regression

두 개의 입력 변수(Petal_Length, Petal_Width)를 사용하여 Iris versicolor와 Iris virginica 두 클래스를 구분하는 로지스틱 회귀 모델을 구현하였습니다. Gradient Descent 알고리즘을 통해 모델 매개변수를 최적화하였으며, 이를 바탕으로 결정 경계를 시각화하였습니다.

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1. Linear Regression

Linear regression is a fundamental machine learning algorithm that is widely used for predicting a continuous target variable based on one or more input variables. It is a simple yet powerful technique that can be applied to a variety of real-world problems, such as predicting housing prices, stock market trends, or sales forecasts. The key advantage of linear regression is its interpretability - the model coefficients can be easily understood and the relationships between the input and output variables can be clearly explained. Additionally, linear regression is computationally efficient and can handle large datasets with relative ease. However, linear regression also has its limitations, as it assumes a linear relationship between the input and output variables, which may not always be the case in complex real-world scenarios. In such cases, more advanced techniques like polynomial regression or regularized regression may be more appropriate. Overall, linear regression remains an essential tool in the data scientist's toolkit and continues to be widely used in both academic and industry settings.
2. Logistic Regression

Logistic regression is a powerful machine learning algorithm used for binary classification problems, where the goal is to predict whether an instance belongs to one of two classes. Unlike linear regression, which is used for predicting continuous target variables, logistic regression is designed to predict a binary outcome, such as whether a customer will churn or not, or whether a loan applicant will default or not. The key advantage of logistic regression is its ability to model the probability of an event occurring, which can be useful for decision-making and risk assessment. Logistic regression also provides interpretable model coefficients, allowing for the identification of the most important predictors in the model. Additionally, logistic regression is relatively simple to implement and can handle a wide range of input variables, including both numerical and categorical features. However, logistic regression also has its limitations, such as its sensitivity to outliers and its inability to capture complex non-linear relationships. In such cases, more advanced techniques like decision trees or neural networks may be more appropriate. Overall, logistic regression remains a widely used and versatile machine learning algorithm, particularly in the fields of finance, healthcare, and marketing.