An Introduction to Statistical Learning Methods

Statistical learning methods are a set of techniques used to learn from data. They can be used to predict outcomes, identify patterns, and make decisions.

There are two main types of statistical learning methods: supervised learning and unsupervised learning.

Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R

by Ashley Hodges Bazer

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Supervised Learning

Supervised learning methods are used to predict outcomes based on a set of input features. The input features are typically numerical, but they can also be categorical.

The output of a supervised learning method is a model that can be used to predict the outcome for new data points.

There are many different types of supervised learning methods, including:

• Linear regression
• Logistic regression
• Decision trees
• Support vector machines
• Neural networks

Unsupervised Learning

Unsupervised learning methods are used to identify patterns in data. The input features are typically numerical, but they can also be categorical.

The output of an unsupervised learning method is a set of clusters or groups of data points.

There are many different types of unsupervised learning methods, including:

• K-means clustering
• Hierarchical clustering
• Principal component analysis
• Factor analysis

Linear and Non-Linear Models

Statistical learning methods can be used to build linear and non-linear models.

Linear models are models that assume that the relationship between the input features and the output is linear. Non-linear models are models that assume that the relationship between the input features and the output is non-linear.

The choice of whether to use a linear or non-linear model depends on the nature of the data.

Model Evaluation

Once a statistical learning model has been built, it is important to evaluate its performance.

The performance of a model can be evaluated using a variety of metrics, including:

• Accuracy
• Precision
• Recall
• F1 score

The choice of which metric to use depends on the specific task that the model is being used for.

Statistical learning methods are a powerful tool for learning from data. They can be used to predict outcomes, identify patterns, and make decisions.

There are many different types of statistical learning methods, and the choice of which method to use depends on the nature of the data and the task that the model is being used for.

I hope this article has provided you with a basic understanding of statistical learning methods. For more information, I recommend reading the following resources:

• Machine Learning by Andrew Ng
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Machine Learning and Data Science: An Introduction to Statistical Learning Methods with R

by Ashley Hodges Bazer

4.7 out of 5

Language	:	English
File size	:	7870 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	284 pages
Paperback	:	24 pages
Item Weight	:	2.88 ounces
Dimensions	:	8.5 x 0.06 x 8.5 inches

FREE