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Guide to Probability Predictions on Multi-class Labels

July 28, 2022

Classification problems are among the most common problems in machine learning. It is, therefore, necessary to understand that there are multiple ways one can solve these problems.

The most common way to solve classification problems is by getting discrete or explicit categorizations as dictated by the nature of the issues in question. This does not always have to be the case as it is possible to solve classification problems by using probability instead of discrete classes of values in a given category. 

By using this method you can assess the likelihood of a given set of features to give the desired label in comparison to other labels.

Additionally, it may also be desirable to make a clear visualization on a table that would compare all these different labels and the probabilities in order to have a more robust understanding of the nature of our predicted data.



This particular kind of problem is known as imbalanced classification. There are a few things that are needed in order to perform successful classifications using this method. Some prior knowledge on loss functions and hyperparameter tuning may be needed. We will learn about imbalance classification but this will be a beginner lesson to introduce the core concept.

The exploratory data analysis and data preprocessing steps required to achieve this will be akin to normal workflow.

Let’s get to it!


  1. Loading the dataset
  2. Performing simple exploratory data analysis.
  3. Using a model that will predict the probabilities.
  4. Arranging the different predictions in a table for proper comparison.

1. Loading the dataset

We will use a common dataset. This will require a time-consuming preprocessing method. The best candidate for this is the famous Iris dataset that could be found either in Scikit-learn or downloaded on Kaggle.

The first step is to call all the libraries that we need.

import pandas as pd
import matplotlib.pyplot as plt
from xgboost import XGBClassifier
from sklearn.preprocessing import 

We will then load the dataset and have a look at the data.

df = pd.read_csv("Iris.csv")
df = pd.drop(["Id"], axis=1) #This drops the Id column that is not needed

The code above gives us a look at the first five rows of our data.

SepalLengthCm | SepalWidthCm | PetalLengthCm | PetalWidthCm | Species
0 |5.1 | 3.5 | 1.4 | 0.2 | Iris-setosa 
1 |4.9 | 3.0 | 1.4 | 0.2 | Iris-setosa 
2 |4.7 | 3.2 | 1.3 | 0.2 | Iris-setosa 
3 |4.6 | 3.1 | 1.5 | 0.2 | Iris-setosa 
4| 5.0 | 3.6 | 1.4 | 0.2 | Iris-setosa 

2. Performing simple exploratory data analysis

The next step is checking whether the data is heavily skewed in any one direction.


The above code gives us four distinct numbers that show that the data is not exceedingly skewed to need skew-reducing transformations.

The values that the above code presents

Another practical step would be to have a look at the numerical values that may be of importance such as mean, standard deviation, and the count of the values. All of this can be achieved by running one line of code.

SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm
count 150.000000 150.000000 150.000000 150.000000
mean 5.843333 3.054000 3.758667 1.198667
std 0.828066 0.433594 1.764420 0.763161
min 4.300000 2.000000 1.000000 0.100000
25% 5.100000 2.800000 1.600000 0.300000
50% 5.800000 3.000000 4.350000 1.300000
75% 6.400000 3.300000 5.100000 1.800000
max 7.900000 4.400000 6.900000 2.500000

The results above allows us to extract a few key insights and trends that will be of use later. For example, from the data provided we could infer that on average, the highest values are in the SepalLengthCm column while the lowest are in the PetalWidthCm column. But this may be of no advantage as we are looking at all three categories of species.

What results would arise from individually describing the different species?

Let’s have a look at Iris-virginica and compare it with Iris-setosa.


#Drops Iris-setosa and Iris-versicolor
df = df[df["Species"].str.contains("Iris-setosa|Iris-versicolor")== False]
SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm
count 50.00000 50.000000 50.000000 50.00000
mean 6.58800 2.974000 5.552000 2.02600
std 0.63588 0.322497 0.551895 0.27465
min 4.90000 2.200000 4.500000 1.40000
25% 6.22500 2.800000 5.100000 1.80000
50% 6.50000 3.000000 5.550000 2.00000
75% 6.90000 3.175000 5.875000 2.30000
max 7.90000 3.800000 6.900000 2.50000


#Drops Iris-virginca and Iris-Versicolor
df = df[df["Species"].str.contains("Iris-virginica|Iris-versicolor")== False]
SepalLengthCm SepalWidthCm PetalLengthCm PetalWidthCm
count 50.00000 50.000000 50.000000 50.00000
mean 5.00600 3.418000 1.464000 2.02600
std 0.35249 0.381024 0.173511 0.10721
min 4.30000 2.300000 1.000000 0.10000
25% 4.80000 3.125000 1.400000 0.20000
50% 5.00000 3.400000 1.500000 0.20000
75% 5.20000 3.675000 1.575000 0.30000
max 5.80000 4.400000 1.900000 0.60000

The insights are now clearer as we are comparing two different categories. There is a distinct difference in the means that highlight that we are dealing with different ranges here.

3. Using a model that will predict the probabilities

My desired model for this is XGBoost as it will quickly get us to a solution that we may desire.

X = df.drop(["Species"], axis=1)
y = df["Species"]
#performing train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=5)
#Training XGBoost
model = XGBClassifier(eval_metric = "mlogloss"), y_train)
y_predict = model.predict_proba(X_test)

After performing train-test-splits to avoid overfitting data we expect three new columns representing the 3 different species and their probability values. We use logloss for this type of prediction in our XGBoost model as it tells us whether our values are close to the true value which is either 0(false) or 1(true).

The first two rows will appear in this manner:

[[2.5100906e-03 9.9584949e-01 1.6403686e-03]
[1.6350182e-02 6.5069902e-01 3.3295083e-01]
[1.0797891e-03 2.8213044e-03 9.9609888e-01] 
[9.9380070e-01 4.2259935e-03 1.9732981e-03] 
[1.3190582e-03 2.5665318e-03 9.9611437e-01]]

They will be in the order of the appearance of unique labels in the “Species” column. Any value greater than 0.5 will be considered the best candidate for the given identity if the model is as accurate as it could be.

But this is not the last step. We may need to present this in a dataset for presentation or submission in a given situation. The next step would be to transfer the probabilities in the array and assign them to unique columns.

4. Arranging the different predictions in a table

y_predict_0 = y_predict[:, 0]
y_predict_1 = y_predict[:, 1]
y_predict_2 = y_predict[:, 2]
#Assigning values to columns and a new file
predicted = pd.DataFrame() #Creates empty dataframe
predicted["Iris-setosa"] = y_predict_0
predicted["Iris-versicolor"] = y_predict_1
predicted["Iris-virginica"] = y_predict_2
predicted.to_csv("predicted.csv", index = False)
.0025100, .9958490, .00164010
.0163500, .6506990, .33295120
.0010800, .0028210, .99609930
.9938010, .0042260, .00197340
.0013190, .0025670, .996114


We now have a file that can be used for submissions if the need ever arose. There is one peculiar problem. Since there are no identification indices, we cannot tell which particular data these observations arose from.

This is specifically because we used test data extracted from the original file. In a situation where both train and test files are offered, there will be no use to drop the identification in the test file.

Hope you found this tutorial useful.

Happy coding!