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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-model-evaluation-in-python/
课程评论：没有评论
## 课程总结：Python 中的机器学习模型评估 本课程专注于使用 Python 编程语言对监督学习模型进行性能评估。在模型训练或超参数调整过程中，评估模型性能至关重要，以判断是否存在过拟合。正确选择性能评估指标，能够显著提升项目价值，反之则可能导致模型不可靠。 **课程内容涵盖：** * **回归模型评估指标：** * R-squared (决定系数) * Mean Absolute Error (平均绝对误差) * Mean Absolute Percentage Error (平均绝对百分比误差) * **二分类模型评估指标：** * 混淆矩阵 (Confusion Matrix) * 精确率 (Precision) * 召回率 (Recall) * 准确率 (Accuracy) * 平衡准确率 (Balanced Accuracy) * ROC 曲线及其面积 (ROC Curve and AUC) * **多分类模型评估指标：** * 准确率 (Accuracy) * 平衡准确率 (Balanced Accuracy) * 宏平均精确率 (Macro Averaged Precision) **学习方式：** 所有课程均以简要理论介绍开始，并以 **Python 编程语言** 和强大的 **scikit-learn 库** 的实际案例结束。课程将在 **Jupyter 环境** 中进行，该环境是数据科学行业的标准。所有 Jupyter Notebooks 均可下载。 **特别说明：** 本课程是“Python 中的监督机器学习”系列课程的一部分，因此部分内容可能与该系列大课程重叠。
In this practical course, we are going to focus on the performance evaluation of supervised machine learning models using Python programming language.After a model has been trained or during hyperparameter tuning, we have to check its performance in order to assess whether it overfits or not. That's why, according to particular projects and needs, we need to select performance metrics carefully. In fact, the choice of the wrong metrics may give us an unreliable model. On the contrary, using the proper performance indicators can lead our project to a higher value.With this course, you are going to learn:Performance metrics for regression models (R-squared, Mean Absolute Error, Mean Absolute Percentage Error)Performance metrics for binary classification models (confusion matrix, precision, recall, accuracy, balanced accuracy, ROC curve and its area)Performance metrics for multi-class classification models (accuracy, balanced accuracy, macro averaged precision)All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.This course is part of my Supervised Machine Learning in Python online course, so you'll find some lessons that are already included in the larger course.
