What are the common mistakes to avoid when using machine learning algorithms?

Common machine learning mistakes include overfitting (model too complex), insufficient data, ignoring feature engineering, not validating results on diverse datasets, and neglecting to address class imbalance if present. Regularly update models as new data becomes available.

In machine learning, common mistakes to avoid include insufficient data, biased datasets that perpetuate existing prejudices, overfitting models to training data and neglecting to thoroughly validate and interpret results. Additionally, ignoring the interpretability of models and failing to consider ethical implications can lead to suboptimal outcomes and reinforce algorithmic biases.

In all model work, overfitting/underfitting have proven the worst enemy, but yet a necessary struggle. Its in the fine balance of avoiding both, that model works prove valuable. All this notwithstanding, all models are at the mercy of data quality assuming the model itself is well understood and articulated. Data gymnastics is an exciting exercise, but can prove futile with poor data quality, result in numerous iterations and displays with minimal valuable outcome. Ultimately, the applicability of any modelling output requires that backing in research aligning theory with practice. A well grounded theory eases applicability, which then matters most in AI development. AI has to match up and challenge natural intelligence in practice.

Writing inefficient code also slows down the performance of the algorithm especially when dealing with large data sets. It may be helpful to spend some time learning about efficient code, and how to check for the efficiency of the code.

Choosing the wrong algorithm in machine learning can have a number of negative consequences, including: 1) Poor Performance 2) Overfitting 3) Underfitting 4) Interpretability Issues 5) Computational Complexity

One of the biggest mistakes that organisations make is to embark on a ML journey without having their data in order. It usually helps to start with metadata and then move on to line level data. Given that data cleansing is a time consuming activity, most organisations perform an impact vs. effort exercise to prioritise areas where they will see the quickest and best results. If your data isn't in the best of quality, ML will not yield good results for you.

A machine learning model is only as good as the data upon which it is trained. Unfortunately, data is rarely delivered in perfect condition. Inaccurate and misleading results can result from missing values, inconsistencies, and biases. I recognize the significance of data quality and devote time and resources to meticulously cleaning and pre-processing my data. This meticulous planning ensures that the algorithm learns from accurate and trustworthy data, paving the way for robust and trustworthy results.

When you have doubts on the quality of inputs, you should not take decisions based in model results. It would be more appropiate to use expert assessment when underlying data is under question

Es esencial que, para obtener conclusiones valiosas de un algoritmo, el conjunto de datos esté libre de ruido y la información sea apropiada y de calidad. Un algoritmo perfectamente desarrollado carece de utilidad si los datos de los que extraerá los parámetros no son idóneos. Además, esto podría llevar a que el algoritmo se base en información sesgada, resultando en conclusiones no solo incorrectas, sino también potencialmente perjudiciales para los derechos y libertades de las personas afectadas. Por lo tanto, realizar un estudio exhaustivo del conjunto de datos utilizado es tan crucial, o incluso más, que el propio desarrollo de la tecnología algorítmica.

The performance of algorithms is significantly affected by data quality. Precise predictions and strong models rely on accurate, dependable data. Biased or flawed data can distort results, diminishing algorithm effectiveness. Quality data enables pattern recognition, adaptability to new situations, and instills confidence in algorithmic results. Meticulous data curation, devoid of errors, is crucial for machine learning algorithms to produce dependable, perceptive, and meaningful outcomes.

Overfitting is every machine learning enthusiast's worst nightmare. It happens when the model becomes overly focused on the training data and loses its ability to generalize to previously unseen examples. The end result? In the real world, predictions become inaccurate and meaningless. Underfitting occurs when a model fails to capture the underlying patterns in the data, resulting in poor performance and missed opportunities. I strike a delicate balance to avoid these extremes. To ensure that my model is neither too rigid nor too flexible, I carefully select model complexity, employ regularization techniques, and closely monitor performance.

Both overfitting and underfitting lead to poor model performance but for different reasons. Balancing between the two is crucial in developing a robust machine learning model. The goal is to achieve a good generalization, where the model performs well on new, unseen data, reflecting its ability to capture the true patterns in the data without being misled by the noise. This is often achieved through a process of model selection and tuning, where you test different models and configurations to find the one that performs best not just on your training data, but more importantly, on validation and test data sets.

I always stick to having a cross-validation. It is similar to self-check a math problem. Go all the way from end to beginning to validate your model. Of course when you can and until you can provide a valid argumentation.

Overfitting and underfitting, the two sides of the generalization coin, represent the delicate balance machine learning models must strike. Imagine a student preparing for a test: overfitting is akin to memorizing every detail, performing flawlessly on practice exams but struggling with the real thing. Underfitting, on the other hand, is like not studying enough, resulting in poor performance across the board. Like a sculptor shaping their art, we, the machine learning practitioners, must find the sweet spot between these extremes. Techniques like regularization act as chisels, gently removing unnecessary details that lead to overfitting. Data augmentation, adding new materials to the sculpture, enriches the model's learning experience.

Common mistakes in machine learning include overfitting (model too complex), underfitting (model too simple), not handling missing data properly, ignoring feature scaling, and not validating the model on diverse datasets. Additionally, using insufficient data, neglecting to interpret results, and blindly applying algorithms without understanding their assumptions can lead to errors. Regularly validating and updating models is crucial to ensure they stay relevant over time.

Ethics in AI is discussed plenty discussed, but rarely fully implemented. The first reason is that it usually requires a robust ML pipeline with automated reporting, alerting, and thresholds. To do this manually for every model at appropriate intervals in untenable. And in my experience, only the companies most dedicated to data science and machine learning have spent the time, money, and effort to fully establish ML Ops with all of the automated bias and drift detection mechanisms for all of their models.

There are two ways model evaluation can help. 1. If the model is evaluated correctly. The model can be tracked on regular intervals and checked if model is working as expected or there is a dip in performance. 2. Regular model evalution startegy if done with human in the loop then more data can be collected easily. Hence the model can be trained further.

While accuracy often takes center stage in machine learning evaluation, it's merely the tip of the iceberg. Responsible and effective model development necessitates a multifaceted approach that delves deeper than single metrics. Evaluating a machine learning model requires a holistic approach that goes beyond narrow metrics. By considering various perspectives, utilizing diverse evaluation methods, and focusing on long-term performance and impact, we can develop models that are not only accurate but also responsible, ethical, and impactful. Remember, the journey to a successful model is an iterative process, and continuous evaluation is key to ensuring its long-term viability and positive contribution to the world.

Building a model is only half the battle. Evaluating its performance is critical to understanding its strengths and weaknesses. To assess the model's capabilities, I employ rigorous evaluation metrics such as accuracy, precision, and recall. This enables me to identify areas for improvement and further refine the model. Furthermore, I test the model on a separate dataset that was not seen during training, ensuring its generalizability and real-world applicability.

A model with a human at heart. Meaning expertise and domain knowledge are vital to overall quality that will impact the interpretation of the model. So we can come up with a human-first machine ideation.

Machine learning algorithms are made or at least double-checked by humans. And with it comes the inevitable: the biases embedded in our centuries-old cerebrum. Now, imagine 285,000 processor cores, 10,000 GPUs, and 400 gigabits per second of connectivity for each graphics card server (e.g., the computing power of ChatGPT in comparison) diffusing our biased information streams, reaching billions of people daily... pretty scary, huh?

Machine learning algorithms, while powerful, lack human intuition, empathy, and ethical judgment. Ignoring the human factor can lead to biased, unethical, or insensitive outcomes, failing to address real-world complexities and human needs.

When utilising machine learning, the one important factor that i remind the team is to remember that the machine is to help us and not replace us because it will only do what it is trained to do but we as humans have the capability to look at the nuances of the matter and make a better decision. In short, the machine coupled with our common sense should help us make better decisions.

Only use the responses from such algorithms as a basic source of information; like an introduction to a subject. It can be useful as a starting point, prompting more in-depth research and analysis from the individual on/from verified platforms/sources. Blindly trusting the information which could come from outdated sources and with possible biases can lead to significant errors.

Well well, as I use to say everything needs a human touch or perspective to adapt for each reality. After all, cultures impact how people be parte or not of some cause.

Beware of using machine learning algorithms to evaluate sensitive information embedded in human interactions. Remember how much valuable human intelligence we lose when generalizing unique reference points. An algorithm without systematic and compassionate human oversight is (de)humanizing. In the past, we were confined by our numbers to spread the information. Nowadays, information highways are accessible to everyone. However, not everyone can modify the content that flows through mass communication channels guided by their respective algorithms. Whose voice does your algorithm represent? What microphones of power does it hold? Are there voices suppressed by your algorithm? Every voice is a narrative, and every narrative is information.

The most important thing is to not ignore the obvious when the statistics/mathematics say otherwise. Most probably, your common sense is better than any model result.

From my technical perspective, I would like to emphasize one of the most important aspects of real-world mature AI & ML solutions. Usually, you are dealing with complex tasks from a business logic POV. That means, for the most enterprise-ready solutions, you can't just pick and plug existing cloud AI services, re-wrap them with your custom UI, and that's it. Usually, to address your problem/domain, you have to come up with a robust and sophisticated architecture that might also incorporate proprietary AI & ML algorithms trained on the company's data. So, all this AI hype is not a magic wand that is going to effortlessly fix all of the existing problems. Experience and expertise of the engineers still stand on the pedestal of this market.

Overfitting: Too many features can degrade model generalization. Complexity: Start simple; complex models aren't always better. Time Series Shuffling: Avoid in time series train and test data split ; it disrupts order. Data Leakage: Causes falsely inflated training performance. Linear Model Constants: Not always necessary, depends on context. Single Metric: Use multiple metrics for a complete performance view.

A silly mistake I made when running a GPR: I was utilizing 49 variables, some of which were derived or calculated values, to project a response variable (in this case, estimating construction speed). In one variable predictor class, I incorrectly utilized actual build speed data to calculate a tangential value. Not realizing this until weeks later, I found that my predictions were abnormally accurate (~98% variance reduction) which was not consistent with the rough model I was developing (expecting 60-70%). Be very cautious when dealing with derived variables for ML algorithms, and take the time to ensure that the responses you’re achieving are both reasonable and realistic.