Classifiers are machine-learning systems built to automate the sorting of data (which could be images, audio, text, or tabular data) into different categories. They are designed to identify the class to which input data belongs and can do so because they have been trained on sufficient numbers of corresponding data points and categories. 

In the first article of this two-part series, you learned what classifiers are, some of their use cases, and how their inputs and outputs are created. You also learned about existing classifiers, different architectures used to build classifiers, and how classifiers interact with the entire machine-learning application. In this article, you will learn how to implement a classifier and several pathways to simplify its creation.

Technical Implementation of a Custom Classifier

To get started, let’s take some time to explore technical details related to classifier implementation. We’ll discuss data types and technology stack options, and then review some technical limitations and options to be aware of when creating a custom classifier.

Limiting the Scope of a Classifier

To limit the scope of a classifier, the classification objective has to be carefully understood. This will help you determine whether the problem should be a binary or multi-class classification problem. This will also help you collect and validate the right kind of data.

Data Types and Techniques

The pathway to be explored when building a classifier usually depends on the nature of the data on which it is to be trained. Here, we’ll outline some common data types and discuss the corresponding processing and classification techniques used for each type.

Tabular Data

This refers to structured data that is typically collected in database tables or spreadsheet documents via Microsoft Excel or Google Sheets. Tabular data is the set of data that can be explicitly understood at the level of each feature (column). 

Regardless, classical machine learning algorithms have proven to be the most optimal modeling methodology. Simpler algorithms such as logistic regression and the K-nearest neighbor classifier can be used to handle smaller datasets. For larger tabular datasets, decision trees, random forests, and gradient-boosting algorithms have been shown to provide better performance. Popular gradient-boosting algorithms include the categorical boosting algorithm, extreme gradient boosting algorithm, and light gradient boosting algorithm

More recently, researchers have been exploring how to optimize deep-learning algorithms for modeling tabular data. Some of these have been seen to outdo classical algorithms. These include:

Revisiting Tabular Deep Learning

PyTorch Tabular

XBNet – Xtremely Boosted Network

TabNet: Attentive Interpretable Tabular Learning

This article is a sample of text data. Text data is usually unstructured and available in the form of sentences. Given that sentences rely on context for their meaning, the way words are arranged matters. Classification systems built for tasks like sentiment analysis, named entity recognition, language identification, etc, must learn to understand this context. To help with this, the raw text data is usually pre-processed, encoded, and used to train an embedding model, which helps to understand the relationship between words and form an implicit context map. Data points (usually sentences or parts of them) are then passed through this embedding model, and the tensors generated are used to train a final classifier. 

Classifying text data is usually done with Long Short Term Memory (LSTM) neural networks or Transformers. LSTM networks use an architecture that determines what information is relevant in the longand short term and what should be forgotten. Thus, context is more easily understood. Transformers were initially designed for natural language processing (NLP) applications. They use the concept of attention to relate different parts of a sequence and generate a representation of it. Transformers have outperformed LSTM networks and are the go-to for complicated tasks.

Audio Data

Audio data encompasses  sound recordings, including spoken words, music, animal sounds, sounds originating in the environment, or any other noise. Preprocessing audio data is very different from the preprocessing of text data. Audio data is usually converted into raw signals, Mel Frequency Cepstrum Coefficients (MFCCs), or Mel spectrograms

Raw signals are obtained by simply loading the audio with parameters such as  the sampling rate already set. On the other hand, MFCCs and Mel spectrograms are computed from the raw signal with Fast Fourier transforms and converted to the mel scale. This is a scale that better represents how humans perceive sound. Libraries that help with analyzing and retrieving features including  the MFCCs and Mel spectrograms from audio files are Essentia, Librosa, TensorFlow signal, and Torchaudio.

In building classifiers to identify speakers, language, music genres, or other tasks, LSTMs are popularly used with MFCCs as the audio feature. CNNs are also used, and, when this is the case, the Melspectrograms are used as features.

Transformers, as previously discussed, are also very useful in audio-based classification as they outperform CNNs and LSTMs. Other approaches are Conditional Generative Adversarial Networks and Convolutional Recurrent Neural Networks.

Video Data

Video data is essentially a combination of audio and image data. This data can be used for object detection and tracking, language identification, etc. Approaches earlier discussed for image and audio data will be useful in building systems to classify videos. This could involve a combination of techniques, depending on the classification objective.

Technology Stack Options for Custom Classifiers

Classifiers are built with various technology stacks that depend on personal preference and performance objectives. Libraries for building classifiers include TensorFlow, PyTorch, Scikit-learn, Theano, and Armadillo, among others. These libraries are written in Python, C++, and CUDA. They also usually have bindings for other languages. 

Technical Limitations

Though intelligent classification systems typically perform well, they can also perform very poorly in some scenarios. One example is when there is a bias in the data gathering process. A model cannot gain information outside the data it is trained on, so if the data is not representative of the real world it is trying to classify, it will perform poorly in production. Also, if the training data set does not contain a lot of samples, deep learning and neural networks are not advised. In that case, classical algorithms can be used.

Another major limitation in creating classifiers is the resources available for training in terms of time and compute. It takes an enormous amount of data to create effective deep learning models, and many teams find that trying to manage and process this amount of data exhausts their resources. In some cases, teams choose to reduce the amount of training data, which makes it difficult to train sufficiently large models; in other cases, there aren’t enough resources available for training to iterate through experiments to find the best solution. This may lead teams to consider a more limited search space size during hyperparameter optimization, which increases the chances of not getting the best solution.

Many machine learning projects are abandoned before they’re completed because the team has underestimated the resources and time that will be required to finish them. 

Classifying Audio and Video Data with is a conversational intelligence platform that simplifies classifying audio and video data. They offer pre-trained machine learning models that can understand the conversational themes and associated sentiments in your data, then use that information to improve speech-to-text functionality. can also assist you in identifying questions that are raised in conversations, provide summaries of conversations, and generate action items that require further discussion or follow up. This allows you to gain a detailed understanding of the conversation’s dynamics without dealing with the enormous overhead of setting up systems to handle all of this yourself. also offers speaker identification and transcription services in over 30 languages, using pre-trained models based on speech-to-text-related techniques. This allows you to understand speakers from various backgrounds, identify their contributions to the conversation, and conduct any additional analysis relevant to your use case.

All of these functionalities are accessible via APIs that can be easily integrated with your applications and offer both real-time and asynchronous access to advanced AI capabilities.


This article taught you about the different implementations of custom classifiers for tabular, image, text, audio, and video data. You have also learned about the technology stack options for building classifiers, the limitations faced when building classifiers, and how to limit the scope of a classifier. 

Finally, you learned about, an organization that provides pre-trained models for conversational media classification to obtain valuable insights and improve the effectiveness of interactive multimedia. Get started with today.

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Fortune Adekogbe
Machine Learning Engineer