Classification models are widely used in data mining to predict the class or category of new data based on existing data. However, not all models are equally accurate, and there are many factors that can affect their performance. In this article, you will learn some tips and techniques to increase the accuracy of your classification model and avoid common pitfalls.
The first step to increase the accuracy of your classification model is to choose the right algorithm for your data and problem. Different algorithms have different strengths and weaknesses, and they may perform better or worse depending on the data size, distribution, complexity, and noise. For example, decision trees are simple and intuitive, but they can be prone to overfitting and instability. Logistic regression is fast and easy to interpret, but it can be limited by linearity and multicollinearity. Support vector machines are powerful and flexible, but they can be computationally expensive and sensitive to parameter tuning. You should compare and evaluate different algorithms based on your data characteristics and performance metrics.
In my experience, the most basic pre-requisite is to have firm grasp of the features used to train the classification model. For this reason, I am biased towards data handling versus hyperparameter tuning. Are there missing values in your features? Have you tested and removed multicollinearity? Do you have a custom approach defined to deal with missing values at a feature level? Do you have sufficient features to begin with? These are some of the most important (and often overlooked!) aspects of classification modelling, which if fixed can have a massive impact. With the best of efforts, your accuracy % may not be what you want it to be, so ensure that there's a feedback loop in place to retrain the model and select new features over time.
There is no "one vs the rest" full proof method to increase accuracy of a classification model, but some most popular and techniques that have worked for most ML engineers and myself are: 1. Feature Engineering: Can you study your features better and make a better decision on feature selection? This is mostly aided by statistical evaluations, domain specific feature engineering, techniques like PCA and scaling your features. 2. Pre-process and clean your data: Take a step back to evaluate your choices and methods to clean/discard your data including how you've handled missing values. 3. Cross Validation: Use K-fold cross validation to assess bias and variance of the model on your data. 4. Fix class imbalance by sampling, if exists.
In most modern platforms, you can compare algorithms using a sample dataset. Leverage this. Don't assume you know the right algo.
The second step to increase the accuracy of your classification model is to preprocess your data properly. Data preprocessing involves transforming, cleaning, and standardizing your data for modeling. This could include handling missing values by removing, replacing, or imputing them, depending on the data quality and quantity. You can also encode categorical variables with label encoding, one-hot encoding, or ordinal encoding. Additionally, you can scale numerical variables with standardization, normalization, or min-max scaling to bring them to a similar range and reduce the effect of outliers and skewness. Finally, feature selection can be done through filter, wrapper, or embedded methods to select the most relevant and informative features for your model and reduce the dimensionality and complexity of your data.
While dataprep is necessary, make sure you are not transforming the data to the extent that it influences the results. Especilly while pruning data or adding synthetic data.
Preprocessing is essential: analogous to cleaning and organizing ingredients before cooking, preprocessing involves cleaning, transforming, and structuring data to remove noise, handle missing values, and standardize formats.
The third step to increase the accuracy of your classification model is to tune your parameters optimally. Parameters are the settings or options that control how the algorithm learns from the data and makes predictions. For example, some common parameters for classification models are the learning rate, which is the rate at which the algorithm updates its weights based on the error gradient; regularization, which is a technique that penalizes the complexity of the model to prevent overfitting and improve generalization; kernel, which is a function that transforms the data into a higher-dimensional space to enable nonlinear separation; and number of neighbors, which is the number of nearest neighbors that are considered for voting or averaging in a k-nearest neighbor algorithm. To find the best combination of parameters for your model, you should experiment with different values and use techniques such as grid search, random search, or Bayesian optimization.
The fourth step to increase the accuracy of your classification model is to validate your model rigorously. Validation is the process of testing your model on unseen data to evaluate its accuracy and performance. You should always split your data into training, validation, and test sets, and use the validation set to fine-tune your model and the test set to assess your final model. You should also use techniques such as cross-validation, bootstrapping, or resampling to reduce the variance and bias of your model and ensure its robustness and reliability. You should also use appropriate metrics such as accuracy, precision, recall, f1-score, roc curve, or confusion matrix to measure your model performance and identify areas of improvement.
The fifth step to increase the accuracy of your classification model is to update your model periodically. Data mining is a dynamic and evolving field, and your data and problem may change over time. You should monitor your model performance and feedback, and update your model accordingly to incorporate new data, features, algorithms, or parameters. You should also use techniques such as online learning, incremental learning, or active learning to enable your model to learn from new data and adapt to changing environments.
The sixth and final step to increase the accuracy of your classification model is to learn from others. Data mining is a collaborative and interdisciplinary field, and you can benefit from the knowledge and experience of other data miners, researchers, and practitioners. You can join online communities, forums, blogs, podcasts, or courses to learn about the latest trends, techniques, and tools in data mining. You can also participate in competitions, challenges, or projects to apply your skills, test your ideas, and get feedback from peers and experts.