Machine learning models are about making accurate predictions about the situations, like Foot Fall in restaurants, Stock-Price, etc. where-as, Statistical models are designed for inference about the relationships between variables, as What drives the sales in a restaurant, is it food or Ambience.
Statistical modeling and machine learning are both approaches used in data analysis, but they have some key differences:
- Goal and Approach:
- Statistical Modeling: The primary goal of statistical modeling is to understand the relationships between variables within a dataset and make inferences about populations based on observed data. It often involves the use of statistical techniques to describe, estimate, and test hypotheses about the data.
- Machine Learning: The primary goal of machine learning is to develop algorithms that can learn from data to make predictions or decisions without being explicitly programmed. It focuses on building models that can generalize well to new, unseen data.
- Flexibility:
- Statistical Modeling: Statistical models are often built based on assumptions about the underlying data distribution and relationships between variables. These assumptions can limit the flexibility of the model and may not always capture complex patterns in the data.
- Machine Learning: Machine learning algorithms are more flexible and can automatically learn complex patterns from data without relying heavily on predefined assumptions. They can handle large and high-dimensional datasets more effectively.
- Model Complexity:
- Statistical Modeling: Statistical models are typically simpler and more interpretable. They often involve explicit mathematical equations that describe the relationships between variables.
- Machine Learning: Machine learning models can range from simple linear models to highly complex models such as neural networks. While complex models can capture intricate patterns in the data, they may lack interpretability.
- Use Cases:
- Statistical Modeling: Statistical modeling is often used in traditional scientific research, hypothesis testing, and inference tasks where understanding the underlying data distribution is important.
- Machine Learning: Machine learning is widely used in various applications such as predictive analytics, pattern recognition, natural language processing, computer vision, and recommendation systems.
- Training Paradigm:
- Statistical Modeling: In statistical modeling, the emphasis is often on parameter estimation using techniques such as maximum likelihood estimation or Bayesian inference.
- Machine Learning: In machine learning, the focus is on algorithmic optimization, where the model learns from data through iterative processes such as gradient descent, backpropagation, or reinforcement learning.
In summary, while both statistical modeling and machine learning involve analyzing data to extract insights, they differ in their goals, approaches, flexibility, complexity, and use cases.