Explainable AI: Interpretability of Machine Learning Models

Why should we trust a machine learning model blindly? Wouldn’t it be wonderful if we can get a better insight into model predictions and improve our decision making? With the advent of explainable AI techniques such as LIME and SHAP, it is no longer a challenge. Nowadays, machine learning models are ubiquitous and becoming a part of our lives more than ever. These models are usually a black box in essence that it’s hard for us to assess the model behavior. From smart speakers with inbuilt conversational agents to personalized recommendation systems, we use them daily, but do we understand why they behave in a certain way? Given their ability to influence our decision, it is of paramount and supreme importance that we should be able to trust them. Explainable AI systems help us understand the inner workings of such models.


**So, What’s Explainable AI?**Explainable AI can be summed up as a process to understand the predictions of an ML model. The central idea is to make the model as interpretable as possible which will essentially help in testing its reliability and causality of features. Broadly speaking, there are two dimensions to interpretability:

  1. Explainability(Why did it do that?)Transparency(How it works?)

Typically, explainable AI systems provide an assessment of model input features and identify the features which are driving force of the model. It gives us a sense of control, as we can then decide if we can rely on the predictions of these models. For instance, we would probably trust a flu identification model more if it considers features like temperature, and cough more significant than other symptoms.**Now that you have an idea of explainable systems, how do we explain model predictions?**There are different ways to do that. LIME is one of them. Let’s squeeze it.

LIME stands for:

Local: Approximate locally in the neighborhood of prediction being explained,

Interpretable: Explanations produced are human-readable,

M_odel-Agnostic: Works for any model like SVM, Neural networks, etc _Explanations: Provides explanations of model predictions.(Local linear explanation of model behaviour)

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Explainable AI: Interpretability of Machine Learning Models
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  • Outbreak Prediction

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Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

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  • Improved Unsupervised Algorithms
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  • Improved Cognitive Services
  • Rise of Robots

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**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

**
Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

Outsource your machine learning solution to India,India is the best outsourcing destination offering best in class high performing tasks at an affordable price.Business** hire dedicated machine learning developers in India for making your machine learning app idea into reality.
**
Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

**Conclusion
**
Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

#hire machine learning developers in india #hire dedicated machine learning developers in india #hire machine learning programmers in india #hire machine learning programmers #hire dedicated machine learning developers #hire machine learning developers