Machine learning algorithms are powerful tools for data analysis, but they can also be prone to errors and biases if not used properly. In this article, you will learn how to avoid some of the most common machine learning algorithm mistakes and improve your results.
One of the first steps in any machine learning project is to choose the appropriate algorithm for your data and your goal. Different algorithms have different strengths, weaknesses, assumptions, and parameters, and you need to consider them carefully before applying them. For example, linear regression is good for predicting continuous variables, but not for classification problems. K-means clustering is good for finding groups of similar data points, but not for finding outliers. Support vector machines are good for high-dimensional data, but not for noisy data. To choose the right algorithm, you need to understand your data, your problem, and your evaluation criteria.
More complex or newer is not always better. Many times it has been proven and documented that people are able get behind things that they understand. When picking a machine learning algorithm, it is good to include, in the decision making process, "how easy will this be to explain? " By having a good understanding of how the algorithm works, you'll not only be able to explain to stakeholders and customers what's going underneath the hood, you'll also be able to determine if the results from the algorithm could potentially make "causal" sense!
There has been an explosion of possible algorithms in recent years. From a craft perspective, the wise data scientist will do a few things to help with choosing an algorithm: 1. They will cleanly identify the problem at hand 2. They will speak with stakeholders to ensure the question being answered is clear, relevant, and (for anything more than a simple analysis) important enough to invest in. The investment decision should be from stakeholders that have the authority to fund a project. 3. With a clear question, they will assess available data. 4. If different data collection is required, they will pivot the project to address this gap. 5. With these structural project needs addressed, they will go through the algorithm selection process.
To make an effective choice, several steps are essential: 1. Understand the problem and define objectives (classification, regression, clustering). 2. Evaluate dataset size and complexity to choose scalable or simpler models. 3. Consider data nature (categorical or numerical) for selecting suitable algorithms. 4. Be aware of algorithm assumptions to avoid inaccurate outcomes. 5. Conduct a comparative analysis of algorithms and assess performance using metrics like accuracy and precision. 6. Account for interpretability to understand the model's decisions and gain insights into the problem. 7. Consider computational resources and time constraints when choosing complex algorithms.
Another crucial step in machine learning is to preprocess your data before feeding it to the algorithm. Preprocessing involves cleaning, transforming, scaling, and encoding your data to make it suitable for the algorithm. For example, you may need to remove missing values, outliers, or duplicates, or replace them with appropriate values. You may need to normalize or standardize your data to make it comparable across features or samples. You may need to encode categorical variables into numerical values, or reduce the dimensionality of your data to avoid the curse of dimensionality. Preprocessing your data can help you improve the accuracy, speed, and robustness of your algorithm.
The challenge for any feature extraction algorithm is to extract and enhance any relevant data features in noisy data or undesirable circumstances and then pass them onto the ML model in order to provide an accurate classification. Unfortunately, many ML models perform badly, due to poor quality data and insufficient training data. An obvious challenge for AIoT is how do we obtain the training data in the first place? In the absence of adequate training data, feature extraction based on science and mathematics is a prudent alternative, as less training data is required, and in general, the quality of the feature estimate is higher as knowledge of the underlying process is used.
A common mistake in machine learning is to use the same data for training and testing your algorithm. This can lead to overfitting, which means that your algorithm performs well on the training data, but poorly on new or unseen data. To avoid overfitting, you need to split your data into three sets: training, validation, and testing. The training set is used to train your algorithm and adjust its parameters. The validation set is used to tune your algorithm and select the best model. The testing set is used to evaluate your final model and estimate its generalization performance. Splitting your data can help you avoid overfitting and underfitting, and optimize your algorithm.
Random split is not the only way to split in practice. There are some better splits in some situations. If you are building some user response models, split by user may be helpful. It will avoid “remember” the same user’s activity lies on both training and validation. If you are building some time series models, split by time is helpful. This approach can help you to validate your model in future time series rather than the past. Stratified split will also be helpful, if the data set is highly skewed by demographics.
You should also consider how you are doing imputation (if you are doing imputation). Imputed values should be determined by the training set only and the methods applied to the test or validation partitions.
Splitting your data is an easy place to get data spoilage. Consider a case where the data is an unbalanced panel - multiple interactions over time. A naive split of such data would include cross-sectional entrants to be in both train and test splits -- meaning that any fixed attributes or even outcomes may show in both sets. The key here is to identify the granularity of data splits, which may or may not be at an individual record level. Making sure your data isn't spoiled will ensure your out-of-sample fit is accurate, and that the performance of your ML artifacts are understandable.
Another important step in machine learning is to validate your results and check their reliability and validity. Validation involves using various methods and metrics to measure how well your algorithm performs on the validation and testing sets, and compare it with other algorithms or benchmarks. For example, you may use cross-validation, bootstrapping, or resampling techniques to estimate the error and variance of your algorithm. You may use accuracy, precision, recall, F1-score, or ROC curve to evaluate your algorithm for classification problems. You may use mean squared error, root mean squared error, or R-squared to evaluate your algorithm for regression problems. Validating your results can help you assess the quality and usefulness of your algorithm.
The final step in machine learning is to interpret your results and communicate them to others. Interpretation involves explaining what your algorithm does, how it works, why it works, and what it means for your data and your goal. For example, you may use feature importance, coefficient, or correlation analysis to identify the most relevant features for your algorithm. You may use confusion matrix, classification report, or decision boundary to visualize and understand how your algorithm classifies your data. You may use residual plot, scatter plot, or regression line to visualize and understand how your algorithm predicts your data. Interpreting your results can help you gain insights and make decisions from your data.
Moving forward with your conclusions may call for another variable to be tested to compare the strength of correlation. Logistic regression using multinomial data may prove to have been the best choice if a new variable is considered to have influenced the outcome. This is true for relating biomarkers to genotype when comparing environmental and inherited factors.
Don't forget George Box's sage advice: "All models are wrong, some are useful." Blindly using just any model because it happens to work on the dataset you have at hand is not a winning strategy. Understand the gaps and gotchas in the models you choose. Also recognize that once models start being used they change the environment they are used in -- as an example, a model intervening in a medical setting to reduce negative outcomes would hopefully appear superficially worse in identifying need for intervention over time due to the actions taken by the the model recommendations!
I often find the most important thing is not the machine learning model chosen or complicated techniques, but rather how you frame the problem at hand and a robust insight into the data available. Generating high quality labels and ensuring data quality for features that are unique and predictive is of utmost importance.
In my experience, I always try to look for multiple models to solve a single issue and based on their initial performance decide to fine-tune anyone. Let say we are doing a binary classification model in that case. Logistic regression decision tree, random forest xgboost all can be used. But the selection of modern soul depend upon the initial best fit model. Model validation is one of the important step in both ITV and OOT. Both this validation will help us to understand the model performance on future data as well as not saying current time data. Ideally, there should not be much fluctuation among development, ITV and OOT.