Kurssisisältö
Data Anomaly Detection
Data Anomaly Detection
2. Statistical Methods in Anomaly Detection
Challenge: Outlier Detection Using MAD Rule
Tehtävä
Swipe to start coding
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:
- Fill in all gaps in
mad()
function to calculate Mean Absolute Deviation. - Calculate the threshold using value
3
as a threshold value. - Specify the rule to detect outliers that will be stored in the
outliers
variable.
Ratkaisu
Oliko kaikki selvää?
Kiitos palautteestasi!
Osio 2. Luku 6
Challenge: Outlier Detection Using MAD Rule
Tehtävä
Swipe to start coding
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:
- Fill in all gaps in
mad()
function to calculate Mean Absolute Deviation. - Calculate the threshold using value
3
as a threshold value. - Specify the rule to detect outliers that will be stored in the
outliers
variable.
Ratkaisu
Oliko kaikki selvää?
Kiitos palautteestasi!
Osio 2. Luku 6