Detecting and Treating Outliers

Treating the odd one out

5 min readMay 15, 2021

Wow, these are lovely! Wait, where does this yellow Tulip come from?

Intro:

One of the most important steps as part of data preprocessing is detecting and treating the outliers as they can negatively affect the statistical analysis and the training process of a machine learning algorithm resulting in lower accuracy.

1. What are Outliers? 🤔

We all have heard of the idiom ‘odd one out’ which means something unusual in comparison to the others in a group.

Similarly, an Outlier is an observation in a given dataset that lies far from the rest of the observations. That means an outlier is vastly larger or smaller than the remaining values in the set.

2. Why do they occur?

An outlier may occur due to the variability in the data, or due to experimental error/human error.

They may indicate an experimental error or heavy skewness in the data(heavy-tailed distribution).

3. What do they affect?

In statistics, we have three measures of central tendency namely Mean, Median, and Mode. They help us describe the data.

Mean is the accurate measure to describe the data when we do not have any outliers present.

Median is used if there is an outlier in the dataset.

Mode is used if there is an outlier AND about ½ or more of the data is the same.

‘Mean’ is the only measure of central tendency that is affected by the outliers which in turn impacts Standard deviation.

Example:

Consider a small dataset, sample= [15, 101, 18, 7, 13, 16, 11, 21, 5, 15, 10, 9]. By looking at it, one can quickly say ‘101’ is an outlier which is much larger than the other values.

computation with and without outlier (Img by author)

From the above calculations, we can clearly say the Mean is more affected than the Median.

4. Detecting Outliers

If our dataset is small, we can detect the outlier by just looking at the dataset. But what if we have a huge dataset, how do we identify the outliers then? We need to use visualization and mathematical techniques.

Below are some of the techniques of detecting outliers

Boxplots
Z-score
Inter Quantile Range(IQR)

4.1 Detecting outliers using Boxplot:

Python code for boxplot is:

import matplotlib.pyplot as plt
plt.boxplot(sample, vert=False)
plt.title("Detecting outliers using Boxplot")
plt.xlabel('Sample')

Detecting outlier using Boxplot (Img by author)

4.2 Detecting outliers using the Z-scores

Criteria: any data point whose Z-score falls out of 3rd standard deviation is an outlier.

How to detect and remove outliers for a machine learning model | LaptrinhX — Detecting Outliers with Z-scores. Image source: https://laptrinhx.com/

Steps:

loop through all the data points and compute the Z-score using the formula (Xi-mean)/std.
define a threshold value of 3 and mark the datapoints whose absolute value of Z-score is greater than the threshold as outliers.

import numpy as np
outliers = []
def detect_outliers_zscore(data):
    thres = 3
    mean = np.mean(data)
    std = np.std(data)
    # print(mean, std)
    for i in data:
        z_score = (i-mean)/std
        if (np.abs(z_score) > thres):
            outliers.append(i)
    return outliers# Driver code
sample_outliers = detect_outliers_zscore(sample)
print("Outliers from Z-scores method: ", sample_outliers)

The above code outputs: Outliers from Z-scores method: [101]

4.3 Detecting outliers using the Inter Quantile Range(IQR)

Criteria: data points that lie 1.5 times of IQR above Q3 and below Q1 are outliers.

steps:

Sort the dataset in ascending order
calculate the 1st and 3rd quartiles(Q1, Q3)
compute IQR=Q3-Q1
compute lower bound = (Q1–1.5*IQR), upper bound = (Q3+1.5*IQR)
loop through the values of the dataset and check for those who fall below the lower bound and above the upper bound and mark them as outliers

outliers = []
def detect_outliers_iqr(data):
    data = sorted(data)
    q1 = np.percentile(data, 25)
    q3 = np.percentile(data, 75)
    # print(q1, q3)
    IQR = q3-q1
    lwr_bound = q1-(1.5*IQR)
    upr_bound = q3+(1.5*IQR)
    # print(lwr_bound, upr_bound)
    for i in data: 
        if (i<lwr_bound or i>upr_bound):
            outliers.append(i)
    return outliers# Driver code
sample_outliers = detect_outliers_iqr(sample)
print("Outliers from IQR method: ", sample_outliers)

The above code outputs: Outliers from IQR method: [101]

5. Handling Outliers

Till now we learned about detecting the outliers. The main question is WHAT do we do with the outliers?

Below are some of the methods of treating the outliers

Trimming/removing the outlier
Quantile based flooring and capping
Mean/Median imputation

5.1 Trimming/Remove the outliers

In this technique, we remove the outliers from the dataset. Although its not a good practice to follow.

Python code to delete the outlier and copy the rest of the elements to another array.

# Trimming
for i in sample_outliers:
    a = np.delete(sample, np.where(sample==i))
print(a)
# print(len(sample), len(a))

The outlier ‘101’ is deleted and the rest of the datapoints are copied to another array ‘a’.

5.2 Quantile based flooring and capping

In this technique, the outlier is capped at a certain value above the 90th percentile value or floored at a factor below the 10th percentile value.

Python code:

# Computing 10th, 90th percentiles and replacing the outliers
tenth_percentile = np.percentile(sample, 10)
ninetieth_percentile = np.percentile(sample, 90)
# print(tenth_percentile, ninetieth_percentile)b = np.where(sample<tenth_percentile, tenth_percentile, sample)
b = np.where(b>ninetieth_percentile, ninetieth_percentile, b)
# print("Sample:", sample)
print("New array:",b)

The above code outputs: New array: [15, 20.7, 18, 7.2, 13, 16, 11, 20.7, 7.2, 15, 10, 9]

The datapoints that are lesser than 10th percentile are replaced with 10th percentile value and the datapoints that are greater than 90th percentile are replaced with 90th percentile value.

5.3 Mean/Median imputation

As the mean value is highly influenced by the outliers, it is advised to replace the outliers with median value.

median = np.median(sample)# Replace with median
for i in sample_outliers:
    c = np.where(sample==i, 14, sample)
print("Sample: ", sample)
print("New array: ",c)
# print(x.dtype)

Visualizing the data after treating the outlier

plt.boxplot(c, vert=False)
plt.title("Boxplot of the sample after treating the outliers")
plt.xlabel("Sample")

Summary:

In this blog, we learnt about an important phase of data preprocessing which is treating outliers. We now know different methods of detecting and treating the outliers.