Conceptos básicos
Máquina de aprendizaje: algoritmo que puede aprender de los
datos sin depender de la programación basado en reglas.
Modelización estadística: formalización de las relaciones
entre las variables en forma de ecuaciones matemáticas.
Diferencias
Tabla 1. Listado de algunos métodos| Estadísticos (Statistics) | Máquinas de aprendizaje (Machine learning) |
| Prueba de hipótesis | Máquinas de Vectores de Soporte |
| Diseño experimental | Redes neuronales |
| Método ANOVA | Árboles de decisión |
| Regresión lineal y logística | Reglas de inducción |
| Modelos lineales generalizados | Métodos de agrupación |
| Análisis de componentes principales | Reglas de asociación |
| Análisis factorial | Algoritmos genéticos |
| Análisis discriminante, etc. | Selección de características, etc. |
Criterio de expertos
Brendan
O'Connor: I know that I’m interested in quantitative information science,
including statistics and data analysis. Machine learning has many strengths,
but it is definitely an odd way to go about analysis. But there’s a good case
that statistics, as traditionally defined, is only going to have a smaller role
in the future. “Data mining” sounds more relevant, but does it even exist as a
coherent subject?
Simon
Blomberg: From R's fortunes package: To paraphrase provocatively, 'machine
learning is statistics minus any checking of models and assumptions'.
Andrew
Gelman: In that case, maybe we should get rid of checking of models and
assumptions more often. Then maybe we'd be able to solve some of the problems
that the machine learning people can solve but we can't!
Referencias
[1] Srivastava, Tavish. (2015). Difference between Machine Learning &
Statistical Modeling. Analytics Vidhya. Disponible en:
http://www.analyticsvidhya.com/blog/2015/07/difference-machine-learning-statistical-modeling/
[2] O'Connor, Brendan. (2009). Statistics vs. Machine Learning, fight!
AI and Social Science. Disponible en: http://brenocon.com/blog/2008/12/statistics-vs-machine-learning-fight/
