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ML Básico – UNIDAD 1

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ML Básico – UNIDAD 1

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Quiz sobre conceptos clave de ML

Created by

Mtra. Maricela Aguilar Illán
Mtra. Maricela Aguilar Illán
Mexico

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ML Básico – UNIDAD 1
 

ML Básico – UNIDAD 1Online version

Quiz sobre conceptos clave de ML

by Mtra. Maricela Aguilar Illán
1

Si la salida y es sí/no, ¿qué tipo de tarea es?

2

Si la salida y es un número como 2450.75, ¿qué tipo es?

3

En aprendizaje supervisado, los datos son:

4

En ML, las features (X) son:

5

En ML, el target (y) es:

6

¿Para qué sirve el conjunto test?

7

En Iris, la pregunta es:

8

Iris es un problema de:

9

Iris tiene:

10

df.head() sirve para:

11

df.info() sirve para:

12

value_counts() sirve para:

13

Accuracy mide:

14

MAE mide:

15

Overfitting significa:

Explicación

La salida binaria suele indicar una etiqueta, no un valor continuo.

Un valor numérico continuo corresponde a regresión.

Las etiquetas permiten aprender la relación entrada–salida.

Las features son las variables de entrada del modelo.

El target es la variable objetivo a predecir.

Evalúa generalización con datos no usados durante el entrenamiento.

Iris es un conjunto de clasificación de especies de flores.

El objetivo es asignar una flor a una especie.

El conjunto Iris tiene tres especies distintas.

Muestra las primeras filas del dataframe.

Proporciona tipos de columnas y presencia de NaN.

Cuenta la frecuencia de cada valor en una columna.

Es la proporción de predicciones correctas.

MAE es la media de los errores absolutos.

El modelo ajusta demasiado a los datos de entrenamiento y no generaliza.

 
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