You want to build machine learning models that are more accurate. What can you do to improve them?

It is a misconception that more data means better result in Machine Learning. It is quality of the data that matters over the quantity. Further the data preparation is not just one time exercise but a continuous process and so the key principles for data preparation are: 1. Quality of data more important than quantity 2. Data preparation is a continuous exercise. Purpose of Data preparation in Machine learning is to 1. Complement missing data. 2. Data format correction 3. Feature engineering 4. Removal of outliers 5. Understanding data distributions 6. Dealing unbalanced data Various data preparation tools to achieve the accurate modelling. 1. Binary Encoding 2. Data Normalization 3. Data Standardization 4. Binning and Discretization

To enhance the accuracy of your machine learning models, start by ensuring a robust and diverse dataset ??. Data quality is key! ??? Use feature engineering to extract more informative attributes from your data ??. Experiment with different algorithms and hyperparameter tuning to find the best fit for your problem ??. Implement cross-validation to evaluate model performance reliably ??. Don't overlook the importance of a proper problem formulation and understanding the underlying data distribution ??. Also, consider ensemble methods like bagging or boosting to improve predictions ??. Stay updated with the latest ML research for new techniques and insights ??.

Cleansing and preparing the data significantly helps boost the accuracy in most real-world scenarios. Before proceeding, you need to know the following: 1. Does your data have Missing Values? - Identify and handle missing values by either removing such rows or interpolating them. 2. Are the Classs skewed in your data? - use techniques like SMOTE or ADASYN. Use random undersampling if there's a huge number of samples. 3. Are the values skewed for some features compared to the rest? - use z-score norm for linear skewness and exponential smoothing for non-linear cases. 4. Are the data types of attributes consistent with what you expect? - use type casting for values to match expected types; encode categorical values into integers.

Effective data preparation significantly influences the performance of machine learning models. It's more beneficial to have high-quality data than to use advanced models or techniques on poor data. The process includes data cleansing to address issues like missing values, outliers, and errors such as duplicates or incorrect values. A deep understanding of the data is essential in this phase. Data transformation, including encoding categorical features and addressing skewness, is the next crucial step. The final phase is feature engineering, where new features are created or redundant columns are removed. Executing these three stages: data cleansing, transformation, and feature engineering enhances the chance of improving model accuracy.

The only most sure shot method to improve ml accuracy is to consider data centric AI approach! In simple words it means that you invest higher percentage of your effort and time on improving the data quality and curating better data for the use case in discussion!

Think about the variables before choosing them. What are the hypotheses they may be trending towards and why they might be important. Answering these in mind before feeding them to ML model can significantly improve the efficacy of the 1st version of the model itself.

To select the model you need to know your use case very well and weigh what are the most important attributes. Is it speed, accuracy, or cost? Once this is defined, you will be in a position to decide. If, for example, speed is key, you can have a custom metric that weights response time more than accuracy finally giving a ponderate sum. With this simple example, you already have a much more representative and useful metric. Always try to customize your metrics to provide extra value!

Meta-learning systems can analyze characteristics of the data and the problem at hand to recommend the most suitable machine learning algorithms. This process involves assessing past performances of various algorithms on similar datasets or tasks and using this information to predict the best algorithm for a new problem.

The use case must be the number one influencer of the algorithm used. Questions like "Is it supervised or unsupervised learning?, Is inference or prediction the endgame?" among others can guide the choice of algorithm. The characteristics of the data too can influence your choice in terms of how sparse the dataset is, # of observations etc. Also, it's important to lean on domain knowledge springing from experts, literature or research that's been published in that space.

Nice put @Joseph. We should always focus on the endgame, thus guiding our decisions in which algorithms to select. I’ve found myself trapped in jumping in modeling part without understanding what’s needed, is the inference more important or predictions? And most of the time could jump into these black box models. Recently I’ve learned that it’s not the model that matters, what matters the most is understanding your business problem which can clearly guides the decision of the algorithm you choose.

One thing I have found helpful is to use the Genetic algorithms in the Hyper parameter tuning. It starts with randomly generated population of hyperparameter sets that undergoes a process simillar to natural selection. The best performing sets and selected and combined using the cross-over and mutation processes to create new sets. This cycle continues over generations, with each new generation being more fit than the previous one. The process ends with a termination condition is met and the most optimal hyperparameter set is selected through the iterative inquiry in the hyper space.

Once you've chosen an algorithm, tune its hyperparameters (adjustable parameters inside the algorithm that control its behavior) to optimize its performance and accuracy. Several methods for hyperparameter tuning are: Grid Search: Evaluates all possible combinations of hyperparameter values within a specified range. Random Search: Randomly samples a subset of hyperparameter combinations from the specified range and evaluates their performance. Bayesian Optimization: Learns from previous evaluations and prioritizes promising hyperparameter combinations. Gradient-Based Optimization: Utilizes gradient descent algorithms to optimize hyperparameters directly, treating them as continuous parameters.

Determining the best hyperparameter tuning technique depends on factors like the complexity of the model, available computational resources, and the size of the dataset. Grid search exhaustively explores a predefined subset of hyperparameters but can be computationally intensive. Random search is more efficient, sampling hyperparameters randomly, while Bayesian optimization constructs a probabilistic model of the objective function, making it effective for expensive evaluations. Genetic algorithms mimic natural selection and can handle non-smooth search spaces. Experimentation and comparison of these techniques are crucial to find the most suitable approach for a given problem.

Grid search is a hyperparameter tuning technique crucial in refining machine learning models. It systematically evaluates all possible combinations of hyperparameter values within predefined ranges or a grid. This exhaustive search aims to identify the configuration that optimizes a specified scoring metric, enhancing the model's accuracy or minimizing errors on validation or test data. Despite its thoroughness, grid search can be computationally expensive, particularly with expansive hyperparameter spaces, but its simplicity and reliability make it a widely utilized tool for optimizing model performance.

Hyperparameters, like learning rate and regularization strength, are configuration settings that influence the training process. For instance, adjusting the learning rate impacts the convergence speed, while regularization strength controls model complexity. In neural networks, experimenting with the number of hidden units, layers, and dropout rates is essential. Tree-based models, such as Random Forest, require tuning the number of trees. Techniques like grid search or random search, coupled with cross-validation, aid in efficiently exploring the hyperparameter space. This iterative process helps identify the optimal set of hyperparameters, enhancing the model's accuracy and generalization across different tasks.

There are various ways to evaluate the performance of a machine learning model, such as accuracy, precision, recall, and F1-score. The confusion matrix is a common method (typically in classification problems) used to visualize the model's prediction accuracy, as it helps easily identify areas of false positives and false negatives. However, it is important to choose the right metric that is aligned with the requirements in the specific problem statement that is being solved to ensure an accurate evaluation of the model.

When trying to improve model accuracy, it is a common mistake to ignore imbalanced dataset issues. For example, you want to predict churn rates, for a dataset of 1000 subscribers( 950 “did not become churn” and 50 “became churn”). This is an imbalanced dataset. Most models would “lazily” predict your customers will not churn, with an accuracy of 95%. Accuracy alone would mislead you. In this specific case, Recall will be very helpful. It is more costly to lose a customer due to a false negative prediction (predicting “will not become churn” when in reality the customer will become churn) than to manage a customer with a false positive prediction (predicting “will become churn”, when the, in reality, the customer did not become churn).

Beyond traditional model evaluation, delving into advanced techniques can significantly uplift your machine learning models. Consider leveraging ensemble methods like bagging or boosting to aggregate predictions from multiple models, thus reducing variance and bias. Additionally, embrace techniques like feature importance analysis to identify and focus on the most impactful variables, refining your model's precision. Experimenting with different algorithm families and embracing domain-specific feature engineering can also uncover hidden patterns, enhancing model accuracy. Remember, a model is only as strong as its understanding of the underlying data, making continuous improvement to new information paramount for sustained accuracy.

Once you have built the model, you need to check how your model is performing on the unseen test data. This process is called model evaluation. For model evaluation, different performance metrics are considered. It is important to know the use case of each of these metrics. For example, Root mean squared error(RMSE) is used for regression models. It can't be used for classification models. For them, we have different metrics like precision, recall, accuracy, F1-score, confusion matrix etc. Also, in case of classification models, if the training dataset is imbalanced, accuracy can be a misleading metric. So, understanding of the problem statement is needed to make a good choice about the performance metrics.

A systematic model evaluation process can make the improvement process more efficient. For examples, 1. Analyzing the types of errors, we can gain insights to model’s weaknesses and focus on specific areas for improvement 2. Identifying feature importance, we can then select or construct a better set of features 3. Analyzing the model’s training process, we can identify any overfitting / under-fitting together with potential bugs in the training process Also, it’s important to notice the following aspects, such as 1. Avoid to overemphasis on a single metric, and select metrics that can be more indicative 2. Align the performance metrics with business goals 3. Identify any data leakage that can result in misleading performance

Model validation is the final validation step before deploying your models into the field. Validating your models against new or external data ensures they can handle diverse scenarios and deliver reliable results in practical applications.

Model validation elevates the precision of the machine learning models, the pivotal fifth step is model validation. This encompasses testing your models on fresh or real-world data, separate from the training, validation, and test sets, to affirm their effectiveness in the problem domain. Utilizing hold-out validation reserves a segment of data pre-training, external validation introduces novel data from external sources, and bootstrap validation entails resampling from your dataset to craft multiple validation sets. The implementation of these validation techniques facilitates thorough testing of models post-training and evaluation, ensuring their resilience and appropriateness for real-world scenarios.

First of all if you have reached to this place, it’s totally great because you have crossed big steps of EDA, and model building. Now coming to validation, it is important to check whether your model performs good or bad on the unseen real-world data. There are different strategies available in ML for validation.

Cross-validation: Employ k-fold cross-validation to assess model performance across different subsets of the dataset. Hyperparameter tuning: Optimize model parameters for better accuracy through techniques like grid search or randomized search. Feature engineering: Enhance model inputs by creating relevant features or transforming existing ones. Ensemble methods: Combine multiple models to leverage their strengths and improve overall predictive performance. Regularization techniques: Apply regularization to prevent overfitting and ensure better generalization on unseen data. Data augmentation: Increase dataset size by generating new examples, especially in image or text-based tasks.

In machine learning, finding the optimum between underfitting and overfitting is the most challenging part. The training error should be small and also the difference between the training and test error should be small. Like everywhere else there is no free lunch in machine learning, every algorithm performs equally on the unobserved data points. If you don't have right input data, you can't build a model. Rather than using complex algorithms firstly we have to filter out useless features in the data set. Also, Occam's razor principle which prefers simple model over a complex model on the same performance level should not be forgotten.

One very helpful practice is to revisit the features list. Features may have to be removed/modified for the following reasons: 1. Obsolete features due to lack of data generation over time 2. Extreme volatility in features tending them to not being very useful 3. Lack of standard deviation over time reducing the capability of classification 4. Tendency of features to correlate with each other due to newly established connections to avoid bias Therefore it is imperative to revise the features list every now and then, train new versions of the models and compare versions for accuracy.

Combining two variables, either through multiplication or division, can enhance model performance. In the auto insurance industry, introducing the variable "DRIVER_PER_CAR" for claim prediction is anticipated to lead to an increased R-square value and a substantial improvement in the model's overall predictive accuracy. This is intuitive, as a higher number of drivers per car is associated with an elevated likelihood of accidents and related risks.

Iterative model improvement involves revisiting previous steps based on insights gained. Regularly analyze misclassified instances to understand model deficiencies and adjust preprocessing steps accordingly. Experiment with different features, explore advanced techniques like transfer learning, and consider model stacking for enhanced performance. Keep an eye on emerging research for novel architectures and methodologies that may be applicable to your specific problem.

Model improvement involves iterating through the machine learning pipeline to refine and enhance the model's performance continuously. This may include collecting more data, engineering new features, exploring different algorithms or architectures, fine-tuning hyperparameters, and addressing issues identified during model evaluation and validation.

Creating or modifying new features or variables derived from existing data to provide additional information and improve model understanding. Adapting or changing the machine learning algorithm to better suit the characteristics of the data and enhance overall model performance. Selecting or defining new evaluation metrics to measure and compare the performance and reliability of models based on specific objectives or requirements.

The main goal of data scientists is to convert their findings into actionable business insights so that businesses can take the right measures toward them. It is very important to quantify the model insights to the business. One of the ways to do that is model explainability. Libraries like Eli5, LIME or SHAPLEY are great ones to help you deliver the story and actionable steps to the stakeholders. Its visualizations will help you put forward you model insights effectively

Another thing to consider is that for most ML models trust is a bigger issue then accuracy. In capital intensive industry, there is generally a lot of distrust of ML models due to their black box nature. Explainable AI (XAI) is the branch of AI which focuses on providing deeper insights on ML models. Including elements of XAI such as LIME or Shapely (SHAP) values will significantly improve the value of your ML models and will also increase the likelihood of long term deployment. I also like the phase approach statement. All ML models will benefit from having a pilot test then move to live deployment. However success of pilot depends on clear KPIs and testing.

While model accuracy measures during model development phase are important, different phases that's crucial after development phase are: - Pilot Test: Once the model is technically live,usually A/B tests are conducted to test how well the model performs in real-time measured in the form of business KPIs.If the model that demonstrated high accuracy metrics during model development phase fails in this phase,incorporating domain expert knowledge and revisiting the development phase can help. - Business live: If model qualifies in the pilot testing phase,it goes to business live phase where it faces complex real world on its own.Iterative feedback rounds with business on relevant KPIs & tackling macro/micro factors helps continuous improvement.

Make sure to try to game the evaluation metric. For instance, in a domain with extremely rare cases, accuracy is great because even the baseline will look great. In areas where you can just classify everything as a positive case, go ahead and evaluate based on recall.

Feature engineering is also a very important aspect for developing an accurate model. No ML algorithm will perform well until you choose the right set of good features.