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Please enable JavaScript in your browser settings or update your browser.Challenge: Outlier Detection Using MAD Rule | Statistical Methods in Anomaly Detection
Data Anomaly Detection
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Contenido del Curso

Data Anomaly Detection

Data Anomaly Detection

1. What is Anomaly Detection?
General InformationTypes of AnomaliesHow Outliers Influence On Prediction ResultsWhat Should We Do With Detected Outliers
2. Statistical Methods in Anomaly Detection
Rule-based ApproachChallenge: Rule-based Approach1.5 IQR Rule3-Sigma RuleMedian Absolute DeviationChallenge: Outlier Detection Using MAD Rule
3. Machine Learning Techniques
ClusteringChallenge: Using DBSCAN Clustering to Detect OutliersRegularisationChallenge: Solving Task Using RegularisationAutoencoders

Challenge: Outlier Detection Using MAD Rule

Tarea

Now, you will use the MAD rule to detect outliers in the California Housing Dataset. It contains various features related to housing characteristics in different districts in California.

In this task, we will detect outliers in the column MedInc, which stands for Median Income.

Your task is to:

  1. Fill in all gaps in mad() function to calculate Mean Absolute Deviation.
  2. Calculate the threshold using value 3 as a threshold value.
  3. Specify the rule to detect outliers that will be stored in the outliers variable.

Tarea

Now, you will use the MAD rule to detect outliers in the California Housing Dataset. It contains various features related to housing characteristics in different districts in California.

In this task, we will detect outliers in the column MedInc, which stands for Median Income.

Your task is to:

  1. Fill in all gaps in mad() function to calculate Mean Absolute Deviation.
  2. Calculate the threshold using value 3 as a threshold value.
  3. Specify the rule to detect outliers that will be stored in the outliers variable.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 6
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Challenge: Outlier Detection Using MAD Rule

Tarea

Now, you will use the MAD rule to detect outliers in the California Housing Dataset. It contains various features related to housing characteristics in different districts in California.

In this task, we will detect outliers in the column MedInc, which stands for Median Income.

Your task is to:

  1. Fill in all gaps in mad() function to calculate Mean Absolute Deviation.
  2. Calculate the threshold using value 3 as a threshold value.
  3. Specify the rule to detect outliers that will be stored in the outliers variable.

Tarea

Now, you will use the MAD rule to detect outliers in the California Housing Dataset. It contains various features related to housing characteristics in different districts in California.

In this task, we will detect outliers in the column MedInc, which stands for Median Income.

Your task is to:

  1. Fill in all gaps in mad() function to calculate Mean Absolute Deviation.
  2. Calculate the threshold using value 3 as a threshold value.
  3. Specify the rule to detect outliers that will be stored in the outliers variable.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Sección 2. Capítulo 6
toggle bottom row

Challenge: Outlier Detection Using MAD Rule

Tarea

Now, you will use the MAD rule to detect outliers in the California Housing Dataset. It contains various features related to housing characteristics in different districts in California.

In this task, we will detect outliers in the column MedInc, which stands for Median Income.

Your task is to:

  1. Fill in all gaps in mad() function to calculate Mean Absolute Deviation.
  2. Calculate the threshold using value 3 as a threshold value.
  3. Specify the rule to detect outliers that will be stored in the outliers variable.

Tarea

Now, you will use the MAD rule to detect outliers in the California Housing Dataset. It contains various features related to housing characteristics in different districts in California.

In this task, we will detect outliers in the column MedInc, which stands for Median Income.

Your task is to:

  1. Fill in all gaps in mad() function to calculate Mean Absolute Deviation.
  2. Calculate the threshold using value 3 as a threshold value.
  3. Specify the rule to detect outliers that will be stored in the outliers variable.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

Tarea

Now, you will use the MAD rule to detect outliers in the California Housing Dataset. It contains various features related to housing characteristics in different districts in California.

In this task, we will detect outliers in the column MedInc, which stands for Median Income.

Your task is to:

  1. Fill in all gaps in mad() function to calculate Mean Absolute Deviation.
  2. Calculate the threshold using value 3 as a threshold value.
  3. Specify the rule to detect outliers that will be stored in the outliers variable.

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 2. Capítulo 6
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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