The sample business proposal for early detection of Breast Cancer which includes defining business goal, success metric, data collection, model selection and eventually deploying model. In Pakistan, it's getting harder and harder to detect breast cancer due to social factors and the availability of radiologists, even then this process requires focus and time which is a luxury if you are in the middle of a pandemic. The radiologist takes a mammogram and then detect tumor or cancer with the naked eye which can be difficult sometimes and error rates are quite high if you are under stress. Out of one thousand women, about one hundred are recalled for additional diagnostic imaging, and of these one hundred women, four or five are diagnosed with breast cancer (nih.gov) (See Figure 1).

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

We feed the test image to the trained model, compares the predicted output with test image's label to evaluate either it's correct or wrong prediction. At the end, we will have the count of correct matches and the incorrect matches. The key realization we need to make, is that in the real world not all incorrect and correct matches hold equal value. Also in the real world, a single metric won't tell the complete story, that's why previously mentioned four metrics are used to evaluate the model. We could organize our predicted values compared to the real values in a confusion matrix.

In this post, you learn how to create a MLOps project to automate the deployment of an Amazon SageMaker endpoint with multiple production variants for A/B testing. You also deploy a general purpose API and testing infrastructure that includes a multi-armed bandit experiment framework. This testing infrastructure will automatically optimize traffic to the best-performing model over time based on user feedback. Amazon SageMaker MLOps projects are a new capability recently released with Amazon SageMaker Pipelines, the first purpose-built, easy-to-use, continuous integration and continuous delivery (CI/CD) service for ML. The MLOps project template provisions the initial setup required for a complete end-to-end MLOps system, including model building, training, and deployment, and can be customized to support your own organizations requirements.

Many modern machine learning applications come with complex and nuanced design goals such as minimizing the worst-case error, satisfying a given precision or recall target, or enforcing group-fairness constraints. Popular techniques for optimizing such non-decomposable objectives reduce the problem into a sequence of cost-sensitive learning tasks, each of which is then solved by re-weighting the training loss with example-specific costs. We point out that the standard approach of re-weighting the loss to incorporate label costs can produce unsatisfactory results when used to train over-parameterized models. As a remedy, we propose new cost-sensitive losses that extend the classical idea of logit adjustment to handle more general cost matrices. Our losses are calibrated, and can be further improved with distilled labels from a teacher model. Through experiments on benchmark image datasets, we showcase the effectiveness of our approach in training ResNet models with common robust and constrained optimization objectives.

This paper analyses the fundamental ingredients behind surrogate explanations to provide a better understanding of their inner workings. We start our exposition by considering global surrogates, describing the trade-off between complexity of the surrogate and fidelity to the black-box being modelled. We show that transitioning from global to local - reducing coverage - allows for more favourable conditions on the Pareto frontier of fidelity-complexity of a surrogate. We discuss the interplay between complexity, fidelity and coverage, and consider how different user needs can lead to problem formulations where these are either constraints or penalties. We also present experiments that demonstrate how the local surrogate interpretability procedure can be made interactive and lead to better explanations.

If you are reading this blog, you will probably be familiar with machine learning or will be interested in learning the same. Machine learning is a subfield of artificial intelligence, where it makes the systems to learn from data and make them capable of taking decisions with minimal human intervention. Now generally, we use the word "model" to indicate this intelligent system. Now, suppose we have a model which is designed to perform a particular task. This task can be anything like, for example, classifying the emails as not spam and spam, or an image classification problem.

The availability of large amounts of user-provided data has been key to the success of machine learning for many real-world tasks. Recently, an increasing awareness has emerged that users should be given more control about how their data is used. In particular, users should have the right to prohibit the use of their data for training machine learning systems, and to have it erased from already trained systems. While several sample erasure methods have been proposed, all of them have drawbacks which have prevented them from gaining widespread adoption. Most methods are either only applicable to very specific families of models, sacrifice too much of the original model's accuracy, or they have prohibitive memory or computational requirements. In this paper, we propose an efficient and effective algorithm, SSSE, for samples erasure, that is applicable to a wide class of machine learning models. From a second-order analysis of the model's loss landscape we derive a closed-form update step of the model parameters that only requires access to the data to be erased, not to the original training set. Experiments on three datasets, CelebFaces attributes (CelebA), Animals with Attributes 2 (AwA2) and CIFAR10, show that in certain cases SSSE can erase samples almost as well as the optimal, yet impractical, gold standard of training a new model from scratch with only the permitted data.

Process mining studies ways to derive value from process executions recorded in event logs of IT-systems, with process discovery the task of inferring a process model for an event log emitted by some unknown system. One quality criterion for discovered process models is generalization. Generalization seeks to quantify how well the discovered model describes future executions of the system, and is perhaps the least understood quality criterion in process mining. The lack of understanding is primarily a consequence of generalization seeking to measure properties over the entire future behavior of the system, when the only available sample of behavior is that provided by the event log itself. In this paper, we draw inspiration from computational statistics, and employ a bootstrap approach to estimate properties of a population based on a sample. Specifically, we define an estimator of the model's generalization based on the event log it was discovered from, and then use bootstrapping to measure the generalization of the model with respect to the system, and its statistical significance. Experiments demonstrate the feasibility of the approach in industrial settings.

Area under the ROC curve is one of the most useful metrics to evaluate a supervised classification model. This metric is commonly referred to as ROC-AUC. Here, the ROC stands for Receiver Operating Characteristic and AUC stands for Area Under the Curve. In my opinion, AUROCC is a more accurate abbreviation but perhaps doesn't sound as nice. In the right context, AUC can also imply ROC-AUC even though it can refer to area under any curve.