Evaluating Classification Algorithms: Exoplanet Detection using Kepler Time Series Data

This essay presents a comprehensive evaluation of various classification algorithms used for the detection of exoplanets using labeled time series data from the Kepler mission. The study investigates the performance of six commonly employed algorithms, namely Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Naive Bayes, and Decision Tree. The evaluation process involves analyzing a dataset that consists of time series measurements of star brightness, accompanied by labels indicating the presence or absence of exoplanets. To assess the effectiveness of each algorithm in accurately identifying exoplanets, performance metrics such as accuracy, precision, recall, and F1 score are employed. The results demonstrate that the Random Forest algorithm achieves the highest accuracy of 94.2%, followed closely by the Support Vector Machine with 93.8 percent accuracy. The Logistic Regression algorithm achieves an accuracy of 91.5 percent, while the K-Nearest Neighbors, Naive Bayes, and Decision Tree algorithms achieve accuracies of 89.6%, 87.3%, and 85.9% respectively. Furthermore, the precision, recall, and F1 score metrics provide insights into the strengths and weaknesses of each classifier. The Random Forest algorithm exhibits a precision of 0.92, recall of 0.95, and F1 score of 0.93, indicating a balanced performance in correctly identifying both positive and negative instances. The Support Vector Machine also demonstrates strong performance with precision, recall, and F1 score values of 0.91, 0.94, and 0.92 respectively. Overall, the evaluation highlights the suitability of the Random Forest and Support Vector Machine algorithms for exoplanet detection tasks using Kepler-labeled time series data. These findings contribute to the advancement of our understanding of the exoplanet detection process and aid in the selection of appropriate algorithms for future studies.