The machine learning validation process is the machine learning equivalent of a full scale roll-out. Machine Learning (ML) projects are often divided into two phases: Data preparation and Model Validation.
During the first phase, machine learning algorithms are applied to selected datasets in order to produce machine learned models; models that use historical data to predict future trends or outcomes. It is during this phase that machine learning engineers create machine algorithms from corporate databases or other machine learning sources.
With enough time, effort and patience, truly effective machine learned models can be developed with accuracy rates approaching nearly 100%. When these algorithms have been tested successfully on historical data sets that represent similar situations as those found in the organization’s current environment, they may then be applied to machines.
In this guide we’ll be talking about how the process is done, what challenges current MLOps teams face, and what the future holds.
Phase 1: The Data Preparation Phase for Machine Learning Validation
The machine learning validation starts with the data preparation phase. This step involves preparing the machine learning dataset for machine learning algorithms. There is a machine learning dataset for machine learning validation purposes. This machine learning dataset is called a training set. The machine-learning algorithms read, process, and learn from this machine-learning dataset to produce models in order to be validated against a test data set.
The training set consists of multiple fields like: an email id, mobile number, age etc. Let us consider that we have to do machine-learning validation with respect to “email id”. So the machine-learning algorithm should only go through those records whose field value is exactly the same as “email id” column values.
Data preparation phase for machine learning validation includes two steps:
This is where you have to specify the machine learning training set from the machine-learning dataset source which is extracted from machine-learning API or websites.
After data scraping, you have to clean your machine learning dataset by removing duplicates and transforming it into machine learning format. You can follow the best practices of machine learning data preparation for this step. For example, make sure that each column has proper type and scale and missing values should be removed also. This process ensures that the machine-learning algorithm does not face any issue related to non-conformity of input variable types or missing variables during the machine-learning validation phase.
Phase 2: Model Validation
After the datasets are prepared — next comes the validation phase. Depending on the machine learning algorithm used and no. of parameters adjusted; machine validation can be very time consuming for a machine learning expert.
Machine Validation Phase: The machine validation phase is where the feedback loops are created so that the machine knows whether or not it is doing well at predicting future data-out-of-sample accurately or not. One method to get machine validation feedback as quickly as possible is to use 10 fold cross-validation [or its equivalent].
In this way, machine validation can start iteratively almost immediately upon building & examining datasets for shape fit (see phase 1, The Data Preparation Phase for Machine Learning Validation). It’s also helpful to show samples of validated machine predictions to some human experts and ask them for their opinions regarding how accurate their model’s predictions seem, after-all model validation does require expert oversight from machine learning engineers from time to time to make sure that they will not trend towards bias.
Example machine validation feedback types from machine models:
– Precision: (ratio of machine predictions that are correct or true, either one is acceptable). e.g. 0.8 (80%), with 1 being every prediction being correct;
– Recall: This machine validation metric deals with the ratio of machine predictions that are known to be true out of all machine predictions made. The number is usually between 0 and 1, but in some machine learning applications that just deal with a lot of different possibilities then it almost always will be higher than 0.9 or even 1.0 (100%). e.g., if the machine model predicts ‘optimistic’ on a large set of customers while only half of them should have actually been predicted as such.
So now that you are aware of how intricate the process of validating machine learning models is — it’s time to ask why the process is significant in the first place and why accuracy is so tantamount.
Where Validation Belongs in the MLOps Priority List
The importance of machine learning model validation cannot be overstated. The machine learning process (especially the training and testing phases) relies on many assumptions. Moreover, machine learning models are based on a mathematical formulation — aimed at reproducing complex relationships that can’t be modeled using deterministic formulas or equations. Hence machine learning models are generally less reliable than their deterministic counterparts.
And since machine learning isn’t an exact science, the results of machine-learning algorithms vary depending on factors like data sampling and distribution (both amongst classes as well as features), hyperparameters, sample size and much more .
Thus, there is bound to be a significant variance in accuracy across various samples. This means machine learning can be subject to bad data and faulty processes no matter how fine-tuned the algorithms are. And machine learning models are known to overfit and underfit at the same time.
To combat this, machine learning vendors have developed a sophisticated process of machine learning validation that involves running several iterations of machine-learning algorithms on different subsets of data (training and test sets) and comparing results with external data sources.
This comparison ensures that training is representative enough for testing purposes, which helps reduce variance in accuracy between various machine-learning models . Validation also ensures that machine-learning algorithms are not overfitting or underfitting relevant patterns in data – another problem machine-learning algorithms face due to their inherent complexity.
Machine Learning Accuracy Vs Validation Accuracy: What Is The Difference?
Machine learning accuracy and validation accuracy, though closely related, are not to be interchanged in applicability. The machine-learning accuracy refers to how well machine-learning algorithms are trained to recognize relevant patterns within training datasets. In contrast, validation accuracy refers to how well machine-learning algorithms perform on a hold out set of data, which is not used for machine learning or training purposes. The difference between machine-learning and validation accuracies is illustrated in the following equation:
Machine Learning Accuracy = (Precision * Recall) / (Precision + Recall)
The machine learning accuracy thus indicates the general level of performance machine learning models have obtained based on their ability to identify potential risks in customer data. The machine learning parameters, precision and recall , refer to the number of true positives (identifying risk factors that actually exist), false negatives (ignoring rejected outputs that are actually true) and false positives (incorrectly indicating the presence of risk factors that do not exist).
The machine learning validation accuracy indicates the degree of confidence machine learning models have at identifying true risks in customer data. The machine learning parameters precision and recall refer to the number of actual occurrences of real fraud cases as well as true negatives (identifying non-fraudulent outputs that are actually non-fraudulent).
Current Challenges In Machine Learning Validation
Despite the process improving drastically over the years, there are some challenges that MLOps face when it comes to the machine learning validation process. These challenges include machine learning being a complex process and machine learning validation being challenging due to the lack of quality machine learning data. The machine learning models should be well thought out, implemented and tested before they are deployed to production to avoid situations where these models provide inaccurate inputs or results.
“How effective machine learning is determined by how much it can improve our business processes.” – Harry Robinson
Machine learning validation is very difficult to get right because machine learning is, afterall, machine generated. The machine does not know which data will work and where to find this data. Therefore, the machine must be extensively tested so it can learn what works best for different scenarios in order to determine what to search for or change how it searches out this information.
Also, it is very difficult to mine quality data for machine learning algorithms because getting quality data could mean possible breaches in user privacy, and it is very difficult to find a machine learning algorithm that is not biased towards a machine that was programmed with bias.
Machine learning validation is also very expensive compared to traditional data analysis because machine learning algorithms take more time to develop and can only be used on specific sets of data as opposed to the entire population of available data, which often leads machine learning companies to spend millions of dollars on research and development.
In addition, machine learning also takes longer to process than traditional data analysis methods because machine learning algorithms must go through a series of steps before it can identify useful information about the machine generated activity.
Also, machine-learning validation requires extensive amounts of computing power due to the amount of calculations required by machine-learning algorithms, leading companies needing a massive server system to support their large datasets.
Despite the challenges in the current methods of machine learning validation, with the higher availability of affordable compute power, and new techniques being developed for capturing rich and high quality data for machine learning models — the future, such as it is, is looking very bright for the machine learning space.