KNN usando R
Teoría
Qué aprenderás y qué es Estadística Inferencial
Valor esperado condicional
Muestras y poblaciones
Muestreo
Estimadores y parámetros
Casos paramétricos y no paramétricos
El espacio de parámetros
Estimación puntual
Estimación por intervalo
Tamaño muestral
Sesgo y varianza
Teoría no paramétrica
Estimación funcional: una sola variable
Estimación funcional: valor esperado condicional
Bootstrapping
Validación cruzada
Introducción a las pruebas de hipótesis
Pruebas de hipótesis
Simulación
Teorías formales
Instalación de R
Explorando datos simulados
Simulando estimadores puntuales
Simulando intervalos de confianza
Observando el comportamiento del tamaño muestral
Estimando distribuciones simuladas
Red neuronal vs. regresión lineal
Examinando el sesgo y la varianza
Haciendo un bootstrapping a un modelo
Hagamos la validación cruzada
Revisemos la potencia de una prueba
Proyecto
Estimación de parámetros con datos reales
Estimación por intervalo de parámetros con datos reales
Red neuronal de pronóstico con datos reales
Validación cruzada de nuestra red neuronal
Calculando el tamaño óptimo de la muestra
Contextualización de la red neuronal
Conclusiones
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Functional estimation of conditional expected value is an essential tool in data analysis. The conditional expected value is interpreted as a function of the independent variable, which can be either linear or nonlinear. Moreover, when the independent variable is categorical, it will present different expected values for different categories. This methodology is not a recent invention; its development rests on deeply rooted mathematical foundations, from Tuki's analysis to Fisher's classical statistics, and even the axioms of probability formulated by Kolmogorov.
There are several commonly used models for the estimation of the conditional expected value, among which the following stand out:
Parametric models: Like linear and logistic regression, these models rely on specific formulas with defined parameters. The coefficients of these parameters are calibrated using established statistical and mathematical methods.
Non-parametric models: In contrast, support vector and K-nearest neighbor machines do not conform to a rigid formula. These techniques cover a broader spectrum of variability in the data, providing estimates through algorithms that attempt to model the complexities of the dependent variable.
It is worth noting that although neural networks are categorized as parametric models due to their structure based on trainable parameters, the magnitude of these parameters is such that, in practice, these models often behave as if they were nonparametric. This feature allows neural networks to capture highly complex patterns in the data, but they also present particular challenges in aspects such as interpretability.
Data science, far from being a mere fad, represents the logical evolution of a century of mathematical and statistical work. The implementation of these models provides the necessary tools to understand and predict behaviors in different fields of knowledge.
Becoming interested in data science and its application with these models can open up a world of opportunities. This knowledge not only enriches your skills, but also puts you in a privileged position to face complex analytical challenges. As the experts would say: Don't stop there! Continue to explore and delve deeper into this fascinating world of functional estimation and statistical inference.
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Questions 1
KNN usando R
Quiero decir aquí a mitad de curso, que es el mejor recurso sobre estadística inferencial que he visto, ni siquiera en la universidad me lo explicaron todo tan claro
Axioma I.
A cada suceso A le corresponde un número no negativo P(A) llamado probabilidad del suceso A.
Axioma II.
P(W) = 1. L a probabilidad del espacio de sucesos elementales W es 1.
Axioma III.
Si A1, A2, … es un conjunto finito o numerable de sucesos incompatibles dos a dos, entonces:
la probabilidad de la unión de todos ellos es igual a la sumatoria de las probabilidades de todos los sucesos.
El valor esperado
condicional es una función
pésimo la clase.
● Regresión logística
● Regresión lineal
● SVM
● KNN
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