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Let’s say the domain is a restaurant kitchen. A dataset with 3 variables. Two predictors and one predicted. Predictor variables are flour in kilograms and water in liters. A predicted variable is the number of roti/ bread. You know the model will be something like this.
Number of roti (y). = b1 * flour in kg (x1) + b2 * water in liter (x2)
Features are scaled to the same scale in terms of the unit of measurement, the KMS system.
During training, your model learns b1 and b2 from training data.
Now during prediction if you enter 1 kg flour and 10 liters water then what should be the number of roti from the model? Or if you input 10 kg flour and 0.1-liter water, then what is the expected output?
Those who have not worked in the kitchen may not understand what I am saying. That is why they say domain knowledge is essential. No problem, try some experiments in the kitchen without someone looking at you. 😊
Now questions are Is there any problem with this model-building approach? Are we missing some steps from data collection to data cleaning to the model building due to which we may get unexpected output? If yes, then where is the problem? What should be the expected output for the above inputs? What needs to be done in this whole approach to build a good model? Can we solve this problem without ML?
Don’t blame the user that you are giving the wrong input and my model is failing.
Any thoughts?
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Dr. Hari Thapliyaal
Dr. Hari Thapliyal is a seasoned professional and prolific blogger with a multifaceted background that spans the realms of Data Science, Project Management, and Advait-Vedanta Philosophy. Holding a Doctorate in AI/NLP from SSBM (Geneva, Switzerland), Hari has earned Master's degrees in Computers, Business Management, Data Science, and Economics, reflecting his dedication to continuous learning and a diverse skill set.
With over three decades of experience in management and leadership, Hari has proven expertise in training, consulting, and coaching within the technology sector. His extensive 16+ years in all phases of software product development are complemented by a decade-long focus on course design, training, coaching, and consulting in Project Management.
In the dynamic field of Data Science, Hari stands out with more than three years of hands-on experience in software development, training course development, training, and mentoring professionals. His areas of specialization include Data Science, AI, Computer Vision, NLP, complex machine learning algorithms, statistical modeling, pattern identification, and extraction of valuable insights.
Hari's professional journey showcases his diverse experience in planning and executing multiple types of projects. He excels in driving stakeholders to identify and resolve business problems, consistently delivering excellent results. Beyond the professional sphere, Hari finds solace in long meditation, often seeking secluded places or immersing himself in the embrace of nature.
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