Exam AI-900 topic 1 question 26 discussion

For me to remember again xD Model Evalution = values Feature engineering = splitting Feature selection = picking

answer is correct

doesn't look like anyone in this thread had this question in their test?

Examining = Evaluation Spliting = new Features = Feature Engineering Picking = Selection = Feature Selection

Maybe these examples help understanding the different concepts: Feature Engineering Example: If you have data about a house's width and length, you could create a new feature called "area" by multiplying the width and length together. This new feature provides additional valuable information that can help the model make better predictions. Feature Selection Example: If you have a list of features such as house width, length, height, and color, feature selection helps you identify which features have the most impact on predicting the house price. By selecting only the key features (e.g., width and length) and discarding less important ones (e.g., height and color), you reduce the complexity of the model without losing predictive power.

1. Model evaluation 2. Feature engineering 3. Feature selection https://learn.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-features?view=azureml-api-1#feature-engineering-and-featurization Although many of the raw data fields can be used directly to train a model, it's often necessary to create additional (engineered) features that provide information that better differentiates patterns in the data. This process is called feature engineering, where the use of domain knowledge of the data is leveraged to create features that, in turn, help machine learning algorithms to learn better.

answer is correct

Evaluation = Confusion Engineering = Splitting Selecting = Picking

Model Evaluation, Featrue Engg, Feature Select

Evaluation = Examining Engineering = Splitting Selection = Picking

The answer is correct: Model Evaluation, Feature Engineering, Feature Selection

Model Evaluation Feature Engineering Feature Selection

Answer is correct

Appeared in today exam, 04/13/2022

Appeared on exam: 03/31/2022. Answers are correct! Model Evaluation Feature Engineering Feature Selection

Model Evaluation Feature Engineering Feature Selection

The answer is correct