Beyond Single Models: The Secret Sauce of Predictive Success

Yashasvi Misra (~yashasvi8)




In this talk, we’ll delve into the fascinating world of ensemble learning techniques in Machine learning —a powerful paradigm that combines the strengths of multiple models to enhance overall predictive performance. Here’s what you can expect: 1. Foundations: Understanding Weak and Strong Learners 2. Bagging: An ensemble technique that has multiple base models trained independently and in parallel. 3. Boosting: A technique that sequentially builds a strong model by combining multiple weak models. 4. Stacking: Model fusion of multiple models, building a more generalized yet robust model.

Join us in this talk, where we’ll demystify these techniques, understand their inner workings, and discover how they elevate predictive performance. Whether you’re a data scientist, machine learning enthusiast, or curious learner, this talk promises insights that go beyond single models!


  1. Basic Machine Learning Knowledge: Familiarity with fundamental concepts like classification, regression, Bias and variance, and model evaluation.
  2. Python Proficiency: Comfortable working with Python and libraries like NumPy, pandas, and scikit-learn.
  3. Understanding of Basic Models: Awareness of models like Decision Trees, SVM, KNN, LogisticRegression etc.
  4. Curiosity and Enthusiasm: A desire to explore ensemble techniques and enhance predictive models.

Speaker Info:

Yashasvi Misra is a Data Engineer at ABInBev, recognized as a GHC Scholar with a solid expertise in Data Modeling, Data Architecture, Python, and Data Science. With a prestigious Excellence Award from Samsung Research India, Yashasvi brings a robust background in research projects and a fervent enthusiasm for exploring and implementing cutting-edge technologies. Passionate about engaging with open source communities, Yashasvi is also a dedicated advocate for diversity and inclusion in the tech industry.

Speaker Links:

PyladiesCon 2023 Host: EuroPython 2021 Talk : PyData Global 2022 talk:

Section: Artificial Intelligence and Machine Learning
Type: Talk
Target Audience: Beginner
Last Updated: