Hello everybody again,
Lat evening while walking home from work, I couldn’t stop thinking about how much more efficient and accurate would be the output if one runs a traditional ML model (lets say Logistic Regression/Random forest/XGBoost etc) as opposed to someone using Deep Learning/NLP etc on the same dataset. What is the distinguishing factor and how does one get to know which one to use for a specific use case?
The choice between traditional machine learning models (like Logistic Regression, Random Forest, XGBoost, etc.) and deep learning models (such as those used in NLP and computer vision) largely depends on the characteristics of the dataset you’re working with.
- Traditional ML Models: If you’re working with structured, tabular data—especially if it’s mostly numeric and the dataset is relatively small—traditional models like XGBoost or Random Forest might perform well and be more efficient. These models are often faster to train and easier to interpret compared to deep learning models.
- Deep Learning Models: On the other hand, deep learning models excel when working with unstructured data, such as text, images, or audio. They are particularly useful for complex patterns and large datasets where traditional models may struggle.
Distinguishing Factor: The key is to understand the nature of your data and the problem you’re trying to solve. It’s often a good practice to start with traditional ML models to establish a baseline performance. If the performance is not satisfactory, or if the dataset is large and unstructured, then you might consider moving on to deep learning models.
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Hi @lukmanaj,
Thank you for the response.
I see, so it’s subjective depending on what we are trying to achieve and the type of data along with how much data we have.
Got it! Appreciate the detailed response.
This really helps.
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