10 Biggest Mistakes in Machine Learning and How to Avoid Them | by Nick Minaie, PhD | Jun, 2023


Your Information to Turning into a Higher Knowledge Scientist

Machine studying has revolutionized numerous industries by enabling computer systems to study from information and make clever choices. Nevertheless, within the journey of constructing machine studying fashions, it’s simple to encounter frequent errors that may hinder progress and result in sub-optimal outcomes. On this weblog submit, we are going to spotlight ten frequent errors in machine studying and supply sensible recommendations on tips on how to keep away from them, making certain smoother and extra profitable mannequin growth.

Photograph by Francisco De Legarreta C. on Unsplash

1 — Inadequate Knowledge Preprocessing

Neglecting information preprocessing steps can have a detrimental affect on mannequin efficiency. For instance, failing to deal with lacking values can introduce bias or result in inaccurate predictions. In a research by Nguyen and Verspoor (2018), they discovered that improper dealing with of lacking information in a gene expression dataset led to vital efficiency degradation within the classification job. Preprocessing strategies like imputation or deletion may be employed to handle lacking information successfully.

One other necessary preprocessing step is function scaling, the place the values of various options are normalized to a typical scale. Neglecting function scaling can lead to sure options dominating the training course of, particularly when utilizing distance-based algorithms like k-nearest neighbors or clustering algorithms. As an example, in a research by Carreira-Perpiñán and Idelbayev (2015), they noticed that failure to scale options led to suboptimal clustering outcomes. Strategies like standardization or normalization may be utilized to scale options appropriately.

Dealing with outliers can also be essential throughout information preprocessing. Outliers can introduce noise and have an effect on the mannequin’s capacity to seize patterns. As an example, in a research by Khan et al. (2020), they discovered that outliers in a credit-scoring dataset led to biased danger evaluation fashions. Strategies like trimming, winsorizing, or utilizing sturdy statistical measures may help mitigate the affect of outliers on mannequin efficiency.

To make sure thorough information preprocessing, it’s important to know the traits of the dataset and make use of acceptable strategies tailor-made to the particular context. By addressing lacking values, scaling options, and dealing with outliers successfully, the standard of the enter information may be improved, main to raised mannequin efficiency.

2 — Lack of Function Engineering

Function engineering is an important step in machine studying that includes reworking uncooked information into informative options that seize related patterns. Failing to carry out function engineering or utilizing incorrect options can restrict mannequin efficiency and the power to uncover invaluable insights.

Contemplate a textual content classification job the place the objective is to categorize buyer critiques as optimistic or destructive. By solely counting on the uncooked textual content information with out function engineering, the mannequin could wrestle to seize necessary indicators of sentiment. Nevertheless, by extracting options akin to phrase frequency, n-grams, or sentiment scores, the mannequin can leverage extra significant representations of the textual content, enhancing classification accuracy.

Function engineering shouldn’t be restricted to numerical or textual information however can even apply to different domains. As an example, in picture classification, extracting options utilizing strategies like convolutional neural networks (CNNs) permits the mannequin to seize hierarchical patterns in photographs. By figuring out edges, textures, and shapes, the mannequin can study extra discriminative representations and make correct predictions.

Furthermore, function engineering can contain domain-specific information and understanding of the issue context. For instance, in fraud detection, area specialists can determine particular patterns or variables which can be indicative of fraudulent transactions. By incorporating such area information into function engineering, fashions can obtain higher efficiency and determine suspicious actions successfully.

Investing time in function engineering requires a deep understanding of the issue area, collaboration with area specialists, and experimentation to determine probably the most informative options. By reworking uncooked information into significant representations, fashions can higher seize patterns and enhance their predictive energy.

3 — Overfitting

Overfitting is a typical mistake in machine studying the place a mannequin performs effectively on the coaching information however fails to generalize to unseen information. This happens when the mannequin turns into overly complicated and begins to memorize the coaching examples moderately than capturing the underlying patterns.

As an example, think about coaching a classification mannequin to differentiate between several types of flowers utilizing numerous options like petal size, petal width, and sepal size. If the mannequin is just too complicated and has too many parameters, it could find yourself memorizing the distinctive traits of every particular person flower within the coaching set moderately than studying the final patterns that distinguish the flower varieties. In consequence, when introduced with new unseen flowers throughout testing, the mannequin will wrestle to make correct predictions.

To keep away from overfitting, a number of strategies may be employed. Regularization strategies, akin to L1 and L2 regularization, introduce a penalty time period to the mannequin’s loss operate, encouraging it to prioritize less complicated options and scale back the affect of overly complicated options. Cross-validation is one other efficient approach the place the info is break up into a number of folds, permitting the mannequin to be educated and validated on completely different subsets of the info. This helps assess the mannequin’s efficiency on unseen information and prevents overfitting by offering a extra dependable estimate of its generalization capacity.

Early stopping can also be extensively used to fight overfitting. It includes monitoring the mannequin’s efficiency throughout coaching and stopping the coaching course of when the efficiency on the validation set begins to deteriorate. By doing so, the mannequin is prevented from overly becoming the coaching information and is as a substitute stopped on the level the place it achieves the perfect stability between coaching and validation efficiency.

By using strategies like regularization, cross-validation, and early stopping, information scientists can mitigate the danger of overfitting, resulting in extra sturdy and generalizable fashions.

4 — Ignoring Mannequin Analysis Metrics

Selecting acceptable analysis metrics is essential for precisely assessing mannequin efficiency and figuring out its effectiveness in fixing the issue at hand. Totally different analysis metrics seize completely different elements of mannequin efficiency, and neglecting them can result in deceptive conclusions or suboptimal choices.

For instance, in a binary classification drawback the place the objective is to foretell whether or not a buyer will churn or not, accuracy alone could not present a complete view of the mannequin’s efficiency. If the dataset is imbalanced and nearly all of clients don’t churn, a mannequin that merely predicts “no churn” for all cases can obtain excessive accuracy however fail to seize the minority class (churned clients). In such circumstances, metrics like precision, recall, F1 rating, or space below the curve (AUC) of the receiver working attribute (ROC) curve must be thought of. These metrics take into account true optimistic, false optimistic, true destructive, and false destructive charges, offering a extra nuanced analysis of the mannequin’s efficiency.

Furthermore, it is very important align the analysis metrics with the particular targets of the issue. As an example, in a medical prognosis job, the price of false negatives (misdiagnosing a sick affected person as wholesome) is perhaps larger than the price of false positives. In such circumstances, optimizing for metrics like sensitivity (recall) turns into extra necessary.

Contemplating the traits of the info, the issue area, and the related prices or priorities, information scientists can choose probably the most acceptable analysis metrics to measure the efficiency of their fashions precisely.

5 — Lack of Ample Coaching Knowledge

Inadequate coaching information is a typical mistake that may hinder the efficiency of machine studying fashions. When the accessible coaching information is restricted or unrepresentative of real-world situations, the mannequin could wrestle to seize the underlying patterns and generalize effectively to unseen information.

As an example, think about coaching a sentiment evaluation mannequin to categorise buyer critiques as optimistic or destructive. If the coaching dataset consists of only some hundred examples, the mannequin could not have sufficient range and variability to study the intricate nuances of language and sentiment. Consequently, the mannequin’s predictions could also be inaccurate or biased when utilized to a bigger and extra numerous dataset.

To handle this subject, information scientists ought to attempt to gather a ample quantity of coaching information that adequately covers the vary of variations and patterns current in the issue area. They will leverage strategies like information augmentation, the place extra artificial examples are generated by making use of transformations or perturbations to the present information. Switch studying is one other strategy that may be helpful when information availability is restricted. By leveraging pre-trained fashions on large-scale datasets, information scientists can extract related options or fine-tune fashions for his or her particular duties, even with smaller datasets.

It’s necessary to notice that the standard of the info is equally essential as the amount. The coaching information must be precisely labeled, free from noise, and consultant of the goal inhabitants. Knowledge preprocessing steps, akin to eradicating duplicates, dealing with lacking values, and addressing information biases, must be carried out to make sure the info’s integrity and reliability.

6 — Failure to Deal with Class Imbalance

Class imbalance happens when the distribution of lessons within the coaching information is considerably skewed, with one class being dominant whereas others are underrepresented. Failing to handle class imbalance can result in biased fashions that favor the bulk class, leading to poor efficiency for the minority class.

For instance, take into account a fraud detection job the place solely a small fraction of transactions are fraudulent. If the coaching information is imbalanced, a mannequin educated on this information could obtain excessive accuracy by merely predicting all transactions as non-fraudulent. Nevertheless, such a mannequin fails to successfully determine uncommon fraudulent transactions, defeating the aim of fraud detection.

To deal with class imbalance, information scientists make use of numerous strategies. Oversampling includes replicating or producing new cases of the minority class to stability its illustration within the coaching information. Undersampling, then again, reduces the variety of cases from the bulk class to match the minority class. These strategies may help the mannequin study from a extra balanced distribution of lessons.

Alternatively, class weighting may be utilized throughout mannequin coaching, assigning larger weights to cases from the minority class. This ensures that the mannequin pays extra consideration to the minority class through the studying course of.

There are additionally superior strategies like ensemble strategies and anomaly detection approaches that may successfully deal with class imbalance. These strategies leverage a mix of fashions or concentrate on figuring out anomalous cases to handle the challenges posed by imbalanced information distributions.

7 — Disregarding Hyperparameter Tuning

Hyperparameters are the configuration settings that decide the conduct and efficiency of machine studying fashions. Failing to correctly tune these hyperparameters can result in suboptimal mannequin efficiency and hinder the power to realize the absolute best outcomes.

As an example, take into account the hyperparameter “studying charge” in a neural community. Setting it too excessive may cause the mannequin to overshoot the optimum resolution and fail to converge, whereas setting it too low can lead to sluggish convergence and longer coaching instances. By neglecting to tune the training charge to an acceptable worth, the mannequin could wrestle to search out the best stability and obtain optimum efficiency.

To handle this error, information scientists ought to discover strategies like grid search, random search, or Bayesian optimization to systematically search the hyperparameter area and determine the perfect mixture of values that maximize mannequin efficiency. Grid search includes specifying a predefined set of hyperparameter values and exhaustively evaluating every mixture, whereas random search randomly samples the hyperparameter area. Bayesian optimization employs a probabilistic mannequin to intelligently discover the area based mostly on earlier evaluations, specializing in promising areas.

Moreover, it’s important to know the affect of every hyperparameter on the mannequin’s conduct and efficiency. Knowledge scientists ought to have a very good grasp of the idea behind the algorithms and their hyperparameters to make knowledgeable choices through the tuning course of. Common experimentation and analysis of various hyperparameter configurations are essential to determine the optimum settings for a given job.

8 — Not Often Updating Fashions

Machine studying fashions shouldn’t be handled as one-time options however moderately as dynamic entities that require common updates and refinements. Failing to replace fashions with new information can lead to degraded efficiency and decreased effectiveness over time.

For instance, think about coaching a suggestion system based mostly on person preferences and conduct. As person preferences evolve, new gadgets are launched, and developments change, the mannequin must adapt to those shifts to offer related suggestions. By neglecting to replace the mannequin with contemporary information and retraining it periodically, the suggestions could change into much less correct and fail to satisfy the altering wants of the customers.

To keep away from this error, information scientists ought to set up processes to commonly retrain and replace fashions with new information. This will contain organising automated pipelines that fetch new information, performing crucial preprocessing steps, and retraining the mannequin on a scheduled foundation. It’s necessary to strike a stability between the frequency of updates and the price of retraining to make sure that fashions keep updated with out incurring extreme computational assets.

Moreover, monitoring the efficiency of the fashions over time is essential. By monitoring key efficiency metrics and evaluating them to baseline efficiency, information scientists can determine when a mannequin’s efficiency begins to degrade and take proactive measures to handle any points.

9 — Lack of Interpretability and Explainability

Interpretability and explainability are essential elements of machine studying, particularly in domains the place transparency and understanding the decision-making course of are important. Neglecting to prioritize interpretability can result in a scarcity of belief within the mannequin’s predictions and hinder its adoption in crucial functions.

As an example, within the medical area, the place affected person well being and well-being are at stake, it is very important perceive why a mannequin made a specific prediction or prognosis. Relying solely on complicated black-box fashions, akin to deep neural networks, with out contemplating interpretability strategies, could make it difficult to offer explanations for the mannequin’s choices.

To handle this error, information scientists ought to discover strategies like LIME (Native Interpretable Mannequin-Agnostic Explanations) or SHAP (SHapley Additive exPlanations) to realize insights into the inside workings of the mannequin. These strategies present explanations on the occasion stage, highlighting the options that contributed probably the most to a specific prediction. By utilizing such strategies, information scientists can present interpretable explanations to end-users or area specialists, enhancing the mannequin’s trustworthiness and facilitating its adoption.

10 — Disregarding the Significance of Area Information

Area information performs a pivotal function in machine studying initiatives. Neglecting to know the issue area can result in improper function choice, insufficient mannequin structure, or misinterpretation of outcomes. Collaboration with area specialists and creating a deep understanding of the issue is essential for making knowledgeable choices all through the complete machine studying pipeline.

For instance, take into account a fraud detection system within the monetary {industry}. With no strong understanding of fraud patterns, regulatory necessities, and industry-specific information, it turns into difficult to determine the related options or design an efficient fraud detection mannequin. Area specialists can present invaluable insights into potential information biases, function engineering strategies, or mannequin analysis standards particular to the {industry}.

To keep away from this error, information scientists ought to actively have interaction with area specialists, set up efficient communication channels, and repeatedly study from their experience. Collaborative efforts can result in the event of extra correct fashions that align with the particular necessities and nuances of the {industry}. Moreover, information scientists ought to make investments time in understanding the issue area via literature critiques, attending {industry} conferences, and taking part in related discussions to remain updated with the most recent developments and challenges within the area.

Remaining ideas…

By being conscious of those frequent errors in machine studying and implementing the instructed methods to keep away from them, information scientists and machine studying practitioners can considerably enhance their mannequin growth course of and obtain extra correct and dependable outcomes. Keep in mind to prioritize information preprocessing, function engineering, and mannequin analysis, and take note of components akin to overfitting, class imbalance, and hyperparameter tuning. Repeatedly study, iterate, and leverage area information to construct sturdy and impactful machine-learning fashions.


  • Nguyen, H. Q., & Verspoor, Ok. (2018). Dealing with lacking values in longitudinal gene expression information. BMC Bioinformatics, 19(1), 9.
  • Carreira-Perpiñán, M. A., & Idelbayev, Y. (2015). Function scaling for clustering. Neural Networks, 67, 114–123.
  • Khan, S., et al. (2020). Coping with outliers in credit score scoring: A survey. Information-Primarily based Programs, 202, 106207.
  • Aggarwal, C. C., & Zhai, C. (2012). Mining textual content information. Springer Science & Enterprise Media.
  • LeCun, Y., et al. (2015). Deep studying. Nature, 521(7553), 436–444.
  • Dal Pozzolo, A., et al. (2015). Calibrating likelihood with undersampling for unbalanced classification. In Symposium on Computational Intelligence and Knowledge Mining (CIDM) (pp. 1–8).
  • Hastie, T., et al. (2009). The weather of statistical studying: information mining, inference, and prediction. Springer Science & Enterprise Media.
  • Bishop, C. M. (2006). Sample recognition and machine studying. Springer.
  • Sokolova, M., et al. (2009). A scientific evaluation of efficiency measures for classification duties. Info Processing & Administration, 45(4), 427–437.
  • Powers, D. M. (2011). Analysis: from precision, recall and F-measure to ROC, informedness, markedness, and correlation. Journal of Machine Studying Applied sciences, 2(1), 37–63.
  • Goodfellow, I., et al. (2016). Deep studying. MIT press.
  • Bengio, Y., et al. (2013). Illustration studying: A overview and new views. IEEE Transactions on Sample Evaluation and Machine Intelligence, 35(8), 1798–1828.
  • Chawla, N. V., et al. (2002). SMOTE: Artificial minority over-sampling approach. Journal of synthetic intelligence analysis, 16, 321–357.
  • He, H., et al. (2009). Studying from imbalanced information. IEEE Transactions on Information and Knowledge Engineering, 21(9), 1263–1284.
  • Bergstra, J., et al. (2012). Random seek for hyper-parameter optimization. Journal of Machine Studying Analysis, 13(Feb), 281–305.
  • Snoek, J., et al. (2012). Sensible Bayesian optimization of machine studying algorithms. In Advances in neural data processing programs (pp. 2951–2959).
  • Hinton, G., et al. (2015). Distilling the information in a neural community. arXiv preprint arXiv:1503.02531.
  • Domingos, P. (2012). Just a few helpful issues to find out about machine studying. Communications of the ACM, 55(10), 78–87.
  • Ribeiro, M. T., et al. (2016). “Why ought to I belief you?”: Explaining the predictions of any classifier. In Proceedings of the twenty second ACM SIGKDD Worldwide Convention on Information Discovery and Knowledge Mining (pp. 1135–1144).
  • Caruana, R., et al. (2015). Intelligible fashions for healthcare: Predicting pneumonia danger and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD Worldwide Convention on Information Discovery and Knowledge Mining (pp. 1721–1730).

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