Amazon Web Services MLS-C01 Exam With Confidence Using Practice Dumps

SageMaker Automatic Model Tuning (AMT) is a fully managed hyperparameter optimization feature that finds the best values for model parameters like eta, alpha, min_child_weight, and max_depth.

“Use SageMaker automatic model tuning to find the best version of a model by running many training jobs on your dataset using the ranges of hyperparameters that you specify.”

It supports built-in algorithms like XGBoost, and can optimize for evaluation metrics like AUC, making it the least operational overhead solution for this task.

Collaborative filtering is a machine learning technique that recommends products or services to users based on the ratings or preferences of other users. This technique is well-suited for identifying customer shopping patterns and preferences because it takes into account the interactions between users and products.

 The best model for predicting housing prices based on a historical dataset with 32 features is linear regression. Linear regression is a supervised learning algorithm that fits a linear relationship between a dependent variable (housing price) and one or more independent variables (features). Linear regression can handle multiple features and output a continuous value for the housing price. Linear regression can also return the coefficients of the features, which indicate how each feature affects the housing price. Linear regression is suitable for this problem because the outcome of interest is numerical and continuous, and the model needs to capture the linear relationship between the features and the outcome.

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Regression vs Classification in Machine Learning

AWS Machine Learning Training - Linear Regression with Amazon SageMaker