本博文是对How to Evaluate Machine Learning Models这一博文的一个简单翻译和总结,文章主要从Evaluation Metrics ,Testing Mechanisms,Hyperparameter Tuning和A/B testing四个角度对机器学习模型的评价做了一一分析和讨论,建议有能力的人直接看原PO文。 1.评价指标(Evaluation Metrics ) 1.1 Classification metrics 假设...
Overfitting refers to an unwanted behavior of a machine learning algorithm used for predictive modeling. It is the case where model performance on the training dataset is improved at the cost of worse performance on data not seen during training, such as a holdout test dataset or new data. We...
First, let’s clarify some basic concepts. Machine learning models are basically mathematical functions that represent the relationship between different aspects of data. For instance, a linear regression model uses a line to represent the relationship between “features” and “target.” The formula ...
How you approach the training of a machine learning depends on the type of model you train. A common approach with traditional models is to iterate through the following steps:Load the data by making it available in the notebook as a DataFrame. Explore the data by visualizing the data and ...
Dedicated SQL pool provides you the capability to score machine learning models using the familiar T-SQL language. With T-SQLPREDICT, you can bring your existing machine learning models trained with historical data and score them within the secure boundaries of your d...
ML.NET gives you the ability to add machine learning to .NET applications, in either online or offline scenarios. With this capability, you can make automatic predictions using the data available to your application without having to be connected to a ne
Discover how to learn machine learning in 2024, including the key skills and technologies you’ll need to master, as well as resources to help you get started.
How does the Test Automation Framework (TAF) for Machine Learning systems look? Testing for Deployment Once you have developed a new version of your model, you need to ensure that the changes do not break anything. To do so, you need to have tests that are ideally triggered on every pull...
Interpreting machine learning models can seem complicated at first, but libraries like SHAP make everything as easy as a function call. We even don’t have to worry about data visualization, as there are built-in functions for that.
The training history of your LSTM models can be used to diagnose the behavior of your model. You can plot the performance of your model using the Matplotlib library. For example, you can plot training loss vs test loss as follows: 1 2 3 4 5 6 7 8 9 10 from matplotlib import pyplot...