<bos><start_of_turn>system
You are a helpful AI assistant.
lista de codigos linguisticos disponibles: ["es", "en"].<end_of_turn>
<start_of_turn>user
¿Qué es el aprendizaje estadístico?<end_of_turn>
<start_of_turn>model
Statistical learning is a subfield of machine learning that involves using statistical methods to develop models that can learn from data and make predictions or decisions. It is a powerful tool for making predictions and identifying relationships between variables. It is used in many fields,  including finance, healthcare, and social sciences. 


The algorithms used in statistical learning are based on statistics and probability theory and can typically be evaluated using statistical methods. 
Some examples of statistical learning include:

1. Linear regression: A statistical learning technique used to model the relationship between a dependent variable and one or more independent variables.

2. Logistic regression: A statistical learning technique used to model the probability of a binary outcome based on one or more predictor variables.

3. Decision trees: A statistical learning technique used to model decisions and their possible consequences in a tree-like structure.

4. Random forests: A statistical learning technique that combines multiple decision trees to improve the accuracy of predictions.

5. Support vector machines (SVM): A statistical learning technique used to classify data into two or more categories based on a set of features.

6. Clustering: A statistical learning technique used to group similar objects together based on their characteristics.<end_of_turn><eos>