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How does machine learning for prediction differ from traditional statistical methods?

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Machine learning and traditional statistics both aim to understand data and make predictions, yet they differ fundamentally in approach and execution. Traditional statistics typically rely on hypothesis testing, p-values, and confidence intervals to make inferences about data. It often involves linear models, like regression analysis, which assume a specific relationship between variables. On the other hand, machine learning encompasses algorithms that learn from data without being explicitly programmed. It's more about prediction than inference, using complex models that can capture non-linear relationships and interactions between variables.

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Americo Cunha

???? Researcher | ?? Professor |???? Board Member | ?? Editor | ?? Applied Mathematics | ??Nonlinear Dynamics | ??…
Nikhil Tiwari

Generative AI | LLM | Certified Monday.com Partner | Make Level 4 Certified.

1 Core Principles

The core principles of traditional statistics involve assumptions about data distribution, such as normality, and the use of parametric tests that rely on these assumptions. In contrast, machine learning methods often make fewer assumptions about data distribution. They can handle large datasets and identify complex patterns that traditional methods might miss. Machine learning algorithms, such as neural networks or decision trees, are designed to improve their performance as they are exposed to more data, a process known as "training."

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Americo Cunha

???? Researcher | ?? Professor |???? Board Member | ?? Editor | ?? Applied Mathematics | ??Nonlinear Dynamics | ?? Artificial Intelligence | ?? Machine Learning
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Machine learning for prediction differs from traditional statistical methods in its approach to model complexity and data handling. Machine learning often uses algorithms that can learn from large and complex datasets without requiring explicit programming or pre-defined assumptions about data distribution, whereas traditional statistical methods typically rely on predefined models and assumptions about the data. This allows machine learning to handle more nuanced patterns and interactions, making it powerful for tasks like image recognition and natural language processing.

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Nikhil Tiwari

Generative AI | LLM | Certified Monday.com Partner | Make Level 4 Certified.
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Machine learning for prediction and traditional statistical methods differ in their approaches, assumptions, and goals. Machine learning is data-driven, focusing on discovering patterns from large datasets without relying on predefined assumptions. It uses complex models like neural networks and decision trees to capture non-linear relationships and prioritizes prediction accuracy, often at the expense of interpretability. In contrast, traditional statistical methods rely on data distribution assumptions, using simpler models like linear regression, which assume linear relationships. relationships between variables, hypothesis testing, and parameter estimation, making them more interpretable but often less flexible.

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Steve Clarke

Senior Data Scientist | Machine Learning Expert | Strategic Leader
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Tree based models and neural networks can easily deal with skewed data, whereas linear models may require a transformation. They also can find non-linear relationships as part of training. Regression modelling required relations to be understood so they can be included in the model formula i.e. polynomials.

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2 Model Flexibility

Traditional statistical models often have a rigid structure, where you choose a model based on theoretical considerations and the data must fit this model. This can limit their ability to capture complex patterns. Machine learning models, like random forests or deep learning networks, are more flexible. They can adapt to the data's underlying structure, even if it's highly non-linear or involves interactions between many variables, leading to potentially more accurate predictions.

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3 Data Requirements

Traditional statistics typically requires smaller datasets and emphasizes the interpretability of models. You might use these methods when you have a strong theoretical understanding of the phenomena being studied. Machine learning thrives on big data. The more data you feed into a machine learning algorithm, the better it can learn the intricacies of the information, making it particularly suited for environments where data is abundant but complex and not well understood.

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Steve Clarke

Senior Data Scientist | Machine Learning Expert | Strategic Leader
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This is important, no model type will always be the correct answer. Simpler models tend to give the power to impact the fit. Bayesian methods allow for prior information (such as expectations from subject matter experts) to be added, which with minimal data can make a huge difference.

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4 Interpretability vs. Accuracy

In traditional statistics, the emphasis is often on model interpretability. You want to understand the relationship between variables and the significance of each predictor. Machine learning models, especially the more complex ones like deep neural networks, often act as "black boxes." While they can provide high predictive accuracy, understanding the exact way they make predictions can be challenging, which is a trade-off you need to consider.

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Steve Clarke

Senior Data Scientist | Machine Learning Expert | Strategic Leader
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Part of building a model is making sure it is used. This often requires non technical buy it. This can be hard to get for black box models. So, often people have greater confidence in an explainable model even if accuracy is slightly lower.

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5 Computational Power

The computational demands of traditional statistical methods are generally lower than those of machine learning techniques. Traditional methods can often be computed relatively quickly and without the need for high-performance computing resources. Machine learning algorithms, particularly those involving large datasets or complex models like deep learning, require significant computational power and may need specialized hardware like graphics processing units (GPUs) to train efficiently.

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Steve Clarke

Senior Data Scientist | Machine Learning Expert | Strategic Leader
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This also means extra time and cost to develop. Depending on the problem this investment may not be worth it. Scaling is easier if using the cloud.

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6 Application Scope

Finally, the application scope of traditional statistics is broad but somewhat confined by the need for theoretical underpinnings and simpler models. Machine learning's application scope is vast and growing, driven by its ability to handle unstructured data like images, text, and sound. It excels in environments where the goal is to make accurate predictions and the models can be constantly refined as new data becomes available.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How does machine learning for prediction differ from traditional statistical methods?

1

2

3

4

5

6

7

1 Core Principles

2 Model Flexibility

3 Data Requirements

4 Interpretability vs. Accuracy

5 Computational Power

6 Application Scope

7 Here’s what else to consider

Statistics

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How does machine learning for prediction differ from traditional statistical methods?

1

2

3

4

5

6

7

1 Core Principles

2 Model Flexibility

3 Data Requirements

4 Interpretability vs. Accuracy

5 Computational Power

6 Application Scope

7 Here’s what else to consider

Statistics

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