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A machine learning model cannot be effective or trustworthy unless it goes through each stage of development. The process begins with data gathering and preparation, followed by data splitting, then model training, testing, and validation.
Deepchecks is a framework that can help speed up the testing and validation of machine learning models and data.
The steps for testing and validating machine learning models and data are outlined in this article, along with a brief introduction to Deepchecks.
Deepchecks is one of the most powerful Python packages which is used for testing and validation of machine learning models.
“Deepchecks,” as the name implies, helps us in deeply examining various aspects for the model’s general performance, data integrity, and much more. This gives us a glimpse of how the machine learning model is created and how the data available would function in changing scenarios with respect to different use cases.
It is one of the most straightforward yet efficient single-shot frameworks that Python provides in the form of an easy-to-install package that can be accessed using pip commands. It makes it simpler to interpret the model’s performance and the various parameters underlying a data discrepancy.
As we already mentioned, one of the Python packages called Deepcheck is used to evaluate the generality and viability of the data and the developed machine learning model. The package’s use is straightforward, but there are some requirements that must be met, as will be discussed below.
What is Testing Data?
Testing Data basically refers to performance that the developed model infers from unseen dataset. For a better performance evaluation of the developed machine learning model, it is frequently possible to use some real-world data. So, in a nutshell, test data is data that is used to evaluate the effectiveness of the created machine learning model.
What is Validation Data?
Validation data is essentially a set of data that is used to test the accuracy of a machine learning model. This data is typically different from the training data and is used to assess how well our model generalizes to new data.
Methods for validating data and machine learning models
Before we get into the inbuilt steps in Deepchecks, let’s look at the checks that Deepchecks have for generating results. In Deepcheck, three types of checks are performed. They are as follows:
There are various checks that take place during the process of testing and validating a machine learning model. These checks include verifying that the data is clean and labels are accurate, checking for data leakage, and assessing the consequences of data imbalance across samples. In addition, common issues associated with data label imbalance are also checked.
The Deepcheck framework uses a collective process known as Suite to test and validate machine learning models and data. This process involves various types of checks, which are performed collectively in a suite. A suite is a group of internal checks that are carried out in the Deepcheck framework.
The data is divided into different proportions of train and test data. The Deepchecks API is responsible for checking the basic issues related to data discrepancies as well as evaluating the model developed for various parameters for genericity for changing data. Therefore, suite internally performs a number of checks and provides a detailed report for the checks performed and the problems with the data and developed machine learning model.
Deepchecks provides an easy way to interpret flaws in data and Machine Learning models, and take action to improve them. The Suite feature is especially useful, as it allows for a detailed check of various aspects of the data and Machine Learning models, and generates interpretable and useful reports.
Additionally, a predefined set of parameters can be used, though some of them may need to be changed depending on the situation. Reports can then be generated to analyze any discrepancies that may exist with the data or the machine learning model that was used for testing and validation. For a better understanding, some of the predefined checks that take place within a suite and its functionality are listed below.
Dataset integrity: The parameter is used to check whether the dataset is accurate and complete.
Train test validation: A set of checks is used to determine if the split of data for the training and testing phases is correct.
Model evaluation: A set of checks is carried out to assess the model performance and genericness, as well as any signs of overfitting.
In this section, we will try to implement Deepcheck from scratch and learn some of the key terms and parameters. Let’s use the iris category classification dataset for this article, which has three classes: iris-setosa, iris-versicolor, and iris-virginica. If we have a single dataset, we can use a single dataset integrity suite for checks to be performed for the data used for the Deepcheck package, or we can use Deepcheck to run a collective check for the entire suite.
Installing Deepcheck package into the working environment
!pip install deepchecks
Importing the required libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as pit
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
Loading the Iris dataset
df = pd.read_csv('iris.csv')
Splitting the data into train and test
output_col = 'Species'
train_df, test_df = train_test_split(df, stratify=df [output_col], random_state=42)
Building a DecisionTreeClassifier for classifying the Iris category
dtc_mod = DecisionTreeClassifier(random_state=42)
dtc_mod.fit(train_ df.drop(output_col, axis=1), train df [output_col]);
Using Deepcheck for tabular data
from deepchecks.tabular import Dataset
train_deepcheck = Dataset(train_df, label=output_col, cat_features=)
test_deepcheck = Dataset(test_df, label=output_col, cat_features=[ ])
Now let’s run the collective process of checks called Suite in Deepcheck for the train and test tabular dataset.
check_suite.run(train_dataset=train_deepcheck, test_dataset=test_deepcheck, model=dtc_mod)
Some of the unused features in the dataset were initially reported in the form of a visual, as shown below, when a full suite check was performed.
Along with this, all additional data was produced in the form of a report that included interpretations of the receiver operating curve (ROC) characteristics, area under the curve (AUC) score, and other data.
For better results and interpretation, only one dataset was used, and only one integrity check was performed. Predictive Power Scores for specific dataset features were reported, showing higher predictive power for some features as a result of data leakage. The Predictive Power Score does not exhibit any signs of data leakage in this use case, though it does fall within a respectable range. The image below helps visualize the same.
Follow the notebook mentioned in the references for a better understanding of all other parameters and problems related to the data used.
When tested and validated for real-time data or changing parameters, Deepcheck recognises and addresses checks for every sensitive parameter and issues that any real-time data and machine learning model would encounter. This helps the machine learning model produce reliable results. This is what makes Deepcheck a user-friendly package for machine learning engineers and developers to use and produce a trustworthy machine learning model for the right outcomes.
DriftScore is a crucial evaluation metric in Deepcheck that aids in comprehending how data and the model created during the deployment and production phases behave. Deepcheck’s use is currently restricted to specific data types and data formats, but it is anticipated that Deepcheck will eventually support even more data types and machine learning models.
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