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

The best solution to meet the requirements is to tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on {“HyperParameterTuningJobObjective”: {“MetricName”: “validation:f1”, “Type”: “Maximize”}}.

The csv_weight hyperparameter is used to specify the instance weights for the training data in CSV format. This can help handle imbalanced data by assigning higher weights to the minority class examples and lower weights to the majority class examples. The scale_pos_weight hyperparameter is used to control the balance of positive and negative weights. It is the ratio of the number of negative class examples to the number of positive class examples. Setting a higher value for this hyperparameter can increase the importance of the positive class and improve the recall. Both of these hyperparameters can help the XGBoost model capture as many instances of returned items as possible.

Automatic model tuning (AMT) is a feature of Amazon SageMaker that automates the process of finding the best hyperparameter values for a machine learning model. AMT uses Bayesian optimization to search the hyperparameter space and evaluate the model performance based on a predefined objective metric. The objective metric is the metric that AMT tries to optimize by adjusting the hyperparameter values. For imbalanced classification problems, accuracy is not a good objective metric, as it can be misleading and biased towards the majority class. A better objective metric is the F1 score, which is the harmonic mean of precision and recall. The F1 score can reflect the balance between precision and recall and is more suitable for imbalanced data. The F1 score ranges from 0 to 1, where 1 is the best possible value. Therefore, the type of the objective should be “Maximize” to achieve the highest F1 score.

By tuning the csv_weight and scale_pos_weight hyperparameters and optimizing on the F1 score, the data scientist can meet the requirements most cost-effectively. This solution requires tuning only two hyperparameters, which can reduce the computation time and cost compared to tuning all possible hyperparameters. This solution also uses the appropriate objective metric for imbalanced classification, which can improve the model performance and capture more instances of returned items.

The most secure action to protect the data from being accessed and transferred to a remote host by malicious code accidentally installed on the training container is to enable network isolation for training jobs. Network isolation is a feature that allows you to run training and inference containers in internet-free mode, which blocks any outbound network calls from the containers, even to other AWS services such as Amazon S3. Additionally, no AWS credentials are made available to the container runtime environment. This way, you can prevent unauthorized access to your data and resources by malicious code or users. You can enable network isolation by setting the EnableNetworkIsolation parameter to True when you call CreateTrainingJob, CreateHyperParameterTuningJob, or CreateModel.

Run Training and Inference Containers in Internet-Free Mode - Amazon SageMaker

The Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm is a supervised learning algorithm that can perform both classification and regression tasks. It can also handle time series data, such as the air quality data in this case. The kNN algorithm works by finding the k most similar instances in the training data to a given query instance, and then predicting the output based on the average or majority of the outputs of the k nearest neighbors. The kNN algorithm can be configured to use different distance metrics, such as Euclidean or cosine, to measure the similarity between instances. To use the kNN algorithm on the single time series consisting of the full year of data, the Machine Learning Specialist needs to set the predictor_type parameter to regressor, as the output variable (air quality in parts per million of contaminates) is a continuous value. The kNN algorithm can then forecast the air quality for the next 2 days by finding the k most similar days in the past year and averaging their air quality values.

Amazon SageMaker k-Nearest-Neighbors (kNN) Algorithm - Amazon SageMaker

Time Series Forecasting using k-Nearest Neighbors (kNN) in Python | by …

Time Series Forecasting with k-Nearest Neighbors | by Nishant Malik …