전체 글63 Different evaluation metrics for classification model Different evaluation metrics for classification models have their own advantages and disadvantages, and their choice depends on the specific requirements and characteristics of the problem at hand. Here are the advantages and disadvantages of some commonly used classification evaluation metrics: 1. Accuracy: Accuracy is the most straightforward metric, measuring the overall correctness of the mo.. 2023. 8. 2. Common regularization techniques in machine learning Regularization is a technique in machine learning that aims to prevent overfitting by adding a penalty term to the loss function. This penalty term discourages complex models that fit the training data too well but may not generalize well to unseen data. Some common regularization techniques in machine learning include: 1. L1 and L2 Regularization: L1 regularization, also known as Lasso regressi.. 2023. 8. 2. Top 10 technical interview question & answer for machine learning engineer Here are the top 10 technical interview questions for a machine learning engineer: 1. What is the difference between supervised and unsupervised learning? - This question aims to test your understanding of the fundamental types of machine learning algorithms. In supervised learning, the model is trained using labeled data, while in unsupervised learning, the model is trained on unlabeled data to.. 2023. 8. 2. 이전 1 ··· 8 9 10 11 다음