Analyticshala | Choose the Right Statistical & ML Model in Predictive Analytics | Regi Mathew

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Dr. Regi Mathew, a fellowship, PhD holder from IIM Ahmedabad, has extensive experience of 20+ years in the data analytics domain. In addition to that, his data science and analytics journey also includes consulting, freelance assignments, and teaching students several topics under the data analytics hood. Without any further ado, let’s unearth the important points that he has discussed in the interview, from how he figured out implementing his learning in the fast-changing world and how the analytics boomed.

What are the commonly used algorithms? How to choose the best one and when?

Considering the constant changes through which the world is going, Dr Regi suggested that the world is inclined towards Artificial intelligence and Machine learning. The two most commonly used algorithms are Statistics based algorithms and Machine Learning -based algorithms. statistics-based is more transparent and gives us the reasoning for explaining the logic behind the end solution. However, ML-based algorithms give predictions that focus on providing much better accuracy, but it does not clearly explain the reason behind the scene. To choose the best one according to your situation, you can take your decision well with more pointers to consider through the stats. based approach if you are looking for the reasoning, whereas you can choose ML if you want to focus more on the accuracy score part. 

Which model to choose and how based on the available options?

Once you have finalized the algorithm, next it comes to which model to choose that will yield the best results. In today’s era, the competition is so tough, and the ML models have become so advanced that the best to choose within the best ML models that you have figured out, for e.g. Random forest, XG boost, etc. and you can go ahead and test them all for selecting the final one based on their accuracy and as per your requirement.

What is Bagging and Boosting?

Bagging stands for bootstrap aggregation, which means we will choose the sample using bootstrap sampling from our data. And, bootstrap sampling means sampling with replacement from the population. In simple words, creating multiple samples from our data and building different models to get their results together to form one algorithm called bagging.

Boosting means building a model, and as per the error of the model, we will build another model by changing the weights and bias to minimize the error for better results, and this is a sequential model building process. We tell the algorithm our requirements, and the model building with the best parameters happens in the back end.

Which are the best ML algorithms so far?

The best Machine Learning algorithms keep on changing based on the scenario and requirement. However, there are some of the best algorithms until today:

  • Random Forest: This is from the bagging family. 
  • XGBoost: It is from the boosting family.
  • CatBoost
  • LightGBM: This is from Microsoft
  • Support Vector Machines: These specialize in packaging techniques.
  • Naive Bayes: It helps in predicting spam emails.

What are some applications of  Deep Learning?

Deep learning is an important factor, and below are a few applications of Deep learning: 

  • Image Recognition
  • Video Recognition
  • Audio Recognition
  • Text-based evaluations

What are the tips for the new Data Science aspirants?

Currently, due to COVID-19, we have a slow economy, but more opportunities to come as the economy rises! Aspirants can use this time to prepare well. Prepare in-depth essential steps, which include:

  1. Basic Statistics
  2. Data Cleaning and Exploratory Data Analysis
  3. Machine Learning algorithms
  4. If possible, explore and get knowledge around Artificial Intelligence and Neural Network.

Keep practicing, get hands-on experience more than just focusing on theories and build a social share circle for your learnings. Focus on one language and get hold of that completely. Watch the complete video to know more about him and learn more about how to prepare yourself for a data science journey. If you are such an enthusiast who wants to kickstart his/her career in data science, then Ivy Professional School has come up with a lot of options and certifications that will help you land your dream job. Contact our wonderful team to start your bright career.

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