Machine learning and deep learning algorithms are all around us in modern businesses. The number of AI applications that may be used has been rapidly increasing with the rapid advancement of new algorithms, cheaper compute, and greater data availability. Every field, from banking to healthcare to education to manufacturing, construction, and beyond, has its own set of machine learning and deep learning solutions.
The biggest problem in all of these ML and DL projects across various sectors is model improvement. So, in this post, we’ll look at methods for improving machine learning models based on structured data (time-series, categorization) and deep learning models based on unstructured data (text, images, audio/video).
Importance of Data Structure
The first thing to understand before we get into strategies for machine learning modeling is to emphasize the importance of data i.e. “what kind of data do you have?”. This is important because ML requires a lot of data in order to train properly. This data must be organized in a way that is easy for the algorithm to understand and use. Data structures provide this organization, making machine learning possible. Without data structures, machine learning would be very difficult, if not impossible. Data must be carefully arranged so that the algorithm can learn from it effectively. Data structures provide this organization, allowing machine learning to take place.
As such, data can be classified into two categories:
- Structured Data — is easier to process and analyze than unstructured data. It’s usually arranged in a fixed format that makes it easy to extract specific pieces of information, which can be helpful for certain types of predictions. For example, if you’re trying to predict how if the price of stock will go up in the next month, you might find it helpful to use data that’s been formatted as a table or spreadsheet. This type of data works best with supervised learning models.
- Unstructured Data — can be a valuable source of information for predictions in machine learning, because it can contain more diverse and nuanced information than structured data. For example, unstructured text data can include information about the sentiment or emotional state of a customer, which might be useful for predicting whether that customer is likely to churn. This type of data works best with unsupervised learning models.
Table 1 — Structured & Unstructured Data Comparison
Machine Learning Algorithms Cheat Sheet
Information in this section provided by SAS Blog to be used for reference only.
Source: SAS Blog — ML Cheat Sheet
How to use the cheat sheet
Read the path and algorithm labels on the chart as “If <path label> then use <algorithm>.” For example:
- If you want to perform dimension reduction then use principal component analysis.
- If you need a numeric prediction quickly, use decision trees or linear regression.
- If you need a hierarchical result, use hierarchical clustering.
Sometimes more than one branch will apply, and other times none of them will be a perfect match. It’s important to remember these paths are intended to be rule-of-thumb recommendations, so some of the recommendations are not exact. Several data scientists I talked with said that the only sure way to find the very best algorithm is to try all of them.
Strategies for Improving ML Models — Structured Data
There are many methods for improving machine learning models based on structured data. Some of the most common methods include:
1. Feature selection: Identifying and selecting the most relevant features from the data can help improve the accuracy of machine learning models. For example, selecting only the most important features from a dataset can help reduce overfitting and improve generalization.
2. Feature engineering: This involves transforming or creating new features from existing ones to better capture relationships in the data. For instance, one could engineer features that capture quadratic or cubic relationships between variables in order to improve the predictive power of a machine learning model.
3. Model selection and tuning: Trying out different machine learning models (e.g., linear regression, decision trees, random forests) and tuning their hyperparameters (e.g., regularization strength, tree depth) can help improve the performance of the final model.
4. Data pre-processing: This step can involve various techniques such as imputation (filling in missing values), outlier removal, and normalization/standardization. Proper data pre-processing can improve the accuracy of machine learning models.
Strategies for Improving ML Models — Unstructured Data
There are various methods for improving machine learning models based on unstructured data. Some of these methods include the following:
1. Using a pre-trained model: A pre-trained model is a machine learning model that has been trained on a large dataset, such as ImageNet. This type of model can be used to improve the performance of a machine learning model that is being trained on a smaller dataset.
2. Using more data: The more data that is available to train a machine learning model, the better the model will perform. This is because more data provides more opportunities for the algorithm to learn from and identify patterns in the data.
3. Training multiple models: Instead of training one single machine learning model, it can be beneficial to train multiple models. This is because each model can learn from different aspects of the data and improve the overall performance of the machine learning system.
4. Ensembling: Ensembling is a technique that combines the predictions of multiple machine learning models to produce a more accurate prediction. This can be done by training multiple models on the same dataset and then taking the average of their predictions, or by training multiple models on different subsets of the data and then taking the majority vote of their predictions.
5. Feature engineering: Feature engineering is the process of creating new features from existing data. This can be done by transforming existing features, such as using PCA to create new features from existing ones, or by creating new features from scratch, such as using the data from an accelerometer to create a new feature that represents the speed of the device.
6. Model tuning: Model tuning is the process of adjusting the hyperparameters of a machine learning model to improve its performance. This can be done by using techniques such as grid search or random search.
7. Regularization: Regularization is a technique that is used to prevent overfitting in machine learning models. This is done by adding constraints to the model, such as limiting the number of parameters that can be used, or by adding penalty terms to the objective function that are associated with large values of the parameters.
8. Data augmentation: Data augmentation is a technique that is used to generate new data from existing data. This can be done by randomly perturbing the existing data, such as adding noise to images or changing the order of words in text documents.
9. Transfer learning: Transfer learning is a technique that is used to learn from other tasks that are related to the task at hand. This can be done by pre-training a machine learning model on a large dataset and then fine-tuning it on the smaller dataset.
10. Dimensionality reduction: Dimensionality reduction is a technique that is used to reduce the number of features that are used to represent the data. The primary benefits of DR includes that it can help to simplify the data, making it easier to work with and understand, it can help to improve the results of machine learning algorithms by reducing the noise in the data and it can also reduce computational costs by reducing the number of features that need to be processed.
Strategies for Improving ML Models — Overall
There are many different ways to improve machine learning and deep learning models. Some common strategies include:
- Using more data: This is often the most important factor in improving a model’s accuracy. The more data you can train your model on, the better it will perform.
- Preprocessing the data: This can help improve the accuracy of your models by removing noise and spurious correlations from the data.
- Manually tweaking the hyperparameters of your algorithms: This can help improve the performance of your models by optimizing them for your specific dataset and task.
- Using ensembles of models: Combining multiple models into an ensemble can often lead to better performance than using a single model.
- Normalization: Normalization is a technique used in machine learning to adjust the range of values in a dataset so that all values are within a certain range. This is often done to make sure that the data can be accurately processed by the machine learning algorithm. There are many different types of normalization, but usually it involves adjusting the data so that the mean value is zero and the standard deviation is one. This ensures that all values in the dataset are normalized within a range of -1 to 1.
- Standardization: Standardization is a process of cleaning and preparing data so that it can be used in machine learning algorithms. This process involves rescaling variables so that they have a mean of 0 and a standard deviation of 1, which ensures that all the variables are in the same scale. Standardization is especially important when you are comparing different machine learning models, as it ensures that all the models are using the same data.
- One-hot encoding: This technique transforms categorical variables into binary vectors. This is useful for datasets with features that are categorical (e.g., gender, race, etc.).
- Understanding the errors: Machine learning models are only as good as the data they are trained on. If you don’t understand what kind of errors your AI model is making, you run the risk of perpetuating inaccurate information and biases. For example, if you have a machine learning model that is classifying images, and it is mistakenly classifying images of black people as gorillas, then you need to be aware of that error so you can fix it. Otherwise, your model will continue to incorrectly classify images, which could have serious implications for real-world applications.
Source: Tech eBay — The six phases of ML modeling and their acceptance criteria
Normalization of Data
Normalization is a machine learning technique that helps to standardize data so that it can be better processed by algorithms. By normalizing data, we can reduce the amount of variability in our dataset, making it more predictable and easier to work with. There are several different techniques for normalizing data, but the most common methods involve rescaling data so that all values lie between 0 and 1, or standardizing data so that each value has a mean of 0 and a standard deviation of 1.
One reason why Normalization is important is because many machine learning algorithms assume that data is normally distributed (i.e. bell-shaped). This means that if our data is not normalized, then these algorithms may not work as well. In addition, normalizing data can help to improve the accuracy of some machine learning algorithms, and can make it easier to compare different datasets.
When to Normalize Data?
Normalization is a feature scaling technique that is used when the data have an unknown distribution or do not have a Gaussian Distribution. This method of data scaling is employed when the data has a broad scope and the algorithms that train the data do not make assumptions about how it will be distributed, such as with an Artificial Neural Network.
Source: Analyst Answer
There are a few different ways to normalize data:
1. Rescaling: This means that all values in the dataset are scaled so that they lie between 0 and 1. To rescale data, we first need to calculate the minimum and maximum values for each feature (column). We then subtract the minimum value from each value in the column, and divide by the range (maximum — minimum).
· Tip: rescaling is a good choice if you want to ensure that all values in your dataset are between 0 and 1.
2. Standardization: This technique transforms data so that it has a mean of 0 and a standard deviation of 1. Unlike rescaling, standardization does not necessarily bound values to a specific range. To standardize data, we first need to calculate the mean and standard deviation for each column. We then subtract the mean from each value in the column, and divide by the standard deviation.
· Tip: Standardization is a good choice if you want to center your data around 0, or if you want to make sure that all values have the same scale.
3. Min-Max Scaling: This is a type of rescaling that transforms data so that all values lie between 0 and 1. Unlike other methods of rescaling, min-max scaling does not center the data around 0. Instead, it scales the data such that the minimum value is 0 and the maximum value is 1. To min-max scale data, we first need to calculate the minimum and maximum values for each column. We then subtract the minimum value from each value in the column, and divide by the range (maximum — minimum).
· Tip: Min-Max Scaling is a good choice if you want to ensure that all values in your dataset are between 0 and 1, but you don’t necessarily want to center the data around 0.
4. Principal Component Analysis (PCA): This is a technique that can be used to reduce the dimensionality of data. It does this by creating new, artificial features that are linear combinations of the original features. These new features are called principal components, and they are ranked in order of importance. The first principal component is the one that explains the most variance in the data, and each subsequent component explains less and less variance. To use PCA to normalize data, we first need to calculate the principle components for our dataset. We then subtract the mean from each value in each column, and divide by the standard deviation.
· Tip: PCA is a good choice if you want to reduce the dimensionality of your data
5. Z-Score Scaling: This is a type of standardization that transforms data so that it has a mean of 0 and a standard deviation of 1. To z-score scale data, we first need to calculate the mean and standard deviation for each column. We then subtract the mean from each value in the column, and divide by the standard deviation.
· Tip: Z-Score Scaling is a good choice if you want to standardize your data without having to calculate the mean and standard deviation for each column.
The method you choose will depend on your dataset and what you want to achieve with it. Whichever method you choose, it’s important to remember that normalizing data is an important step in preprocessing data for machine learning. Without normalization, some machine learning algorithms may not work as well, and it may be more difficult to compare different datasets.
Best Practices for ML Algorithms
The best practices for using machine learning algorithms vary depending on the problem you’re trying to solve. However, some general best practices include:
- Choose the right algorithm: Choosing the right algorithm for your data is important, as it can affect the results that you get. Three of the most common ML algorithms are linear regression, decision trees, and Naive Bayes. For example, linear regression is good for predicting values based on a set of known inputs, while clustering is good for grouping data into clusters.
- Data preparation: This is one of the most important aspects of machine learning (ML). Without clean and feature-rich data, it is very difficult to train accurate ML models. Data preparation includes tasks such as identifying and dealing with outliers, filling in missing values, creating new features from existing data, etc. All of these tasks require a deep understanding of the data and the ML algorithms that will be used to train the model. Every machine learning algorithm has different requirements for the input data. For example, some algorithms can deal with missing values better than others. Some can work with categorical data while others require numerical data. So, it is important to select the right algorithms for your data and prepare the data accordingly.
- Preprocess your data: By preprocessing your data, you can ensure that your algorithm is working with clean and consistent data. This can drastically improve the performance of your algorithm. Additionally, preprocessing your data can help to reduce noise and remove outliers. This can again improve the performance of your machine learning algorithm
- Train your model carefully: Don’t overfit your data; choose an appropriate number of layers and parameters for your model, and use cross-validation to test its accuracy.
- Evaluate your results: Always evaluate your results to see how well your machine learning algorithm is performing. This will help you fine-tune your algorithms and ensure they’re working as effectively as possible.
- Tune your model: Once you’ve chosen and configured your algorithm, you need to tune it for optimal performance. This includes finding the right combination of parameters for your data and your problem.
- Deploy your model: It is important to deploy your model in a machine learning algorithm in order to make predictions or classifications. The algorithms will be able to use the model to more accurately predict outcomes or classify objects. Additionally, the deployment of the model will help improve performance and optimize the results of the machine learning process.
- Retrain your model: As your data changes over time, you’ll need to retrain your model to keep it accurate. There are a few different ways to retrain your model. One way is to simply start from scratch with a new training set. This can be time-consuming, but it gives you the opportunity to completely revamp your model if needed. Another way is to incrementally update your existing model using only the new data points. This is often more efficient, but it can lead to suboptimal results if not done correctly.
Machine learning optimization is important for a number of reasons. First, it can help improve the accuracy of your models. Second, it can help you reduce the amount of training data needed to train your models. Third, it can help you enable faster and more efficient training of your models. Finally, machine learning optimization can help you avoid overfitting your models to the training data.
Machine learning optimization is a process that helps you select the best possible settings for your machine learning algorithms so that they will perform well on new data. The process involves finding the combination of algorithm settings that results in the highest accuracy on a validation set or test set.
There are a few different types of optimization techniques you can use for machine learning models: grid search, random search, and Bayesian search.
1. Exhaustive search, also known as brute-force searching, is the act of examining each potential hyperparameter to see whether it is a suitable match. When you forget the code for your bike’s lock and try out all of the possible options, you’re doing something similar in machine learning. The basic approach is straightforward. If you’re using a k-means algorithm, for example, you’ll have to search for the suitable number of clusters manually. However, if there are hundreds or thousands of alternatives to consider, it becomes too time consuming and heavy. In most real-world scenarios, brute-force search is ineffective.
2. Gradient descent is the most common approach for model advancement in order to reduce error. You must iterate over the training data and re-train the model at each iteration to implement gradient descent. Because it shows that you can achieve the lowest possible error while also improving the model’s accuracy, you want to minimize the cost function.
3. Generic Algorithms is an idea to apply evolution theory to machine learning. Only those organisms that have the greatest adaptation mechanisms survive and reproduce in the evolution theory. In machine learning, how do you determine which specimens are and aren’t the best?
Consider you’ve got a collection of unstructured algorithms. This will be your population. Some models are superior suited than others, and there are a variety of different models with some predetermined hyperparameters. Let’s see how we do it! To begin, you evaluate the accuracy of each model first. Then, only those that performed best are kept and used to generate new models by combining their parameters randomly. The new models are evaluated and the cycle repeats until we have a model that generalizes well.
Genetic algorithms are interesting because they can optimize a solution without being given any information about the problem other than what is necessary to evaluate candidate solutions. This is different from most optimization techniques, which require derivatives or some other form of problem-specific information.
Deep learning and machine learning require a high level of subject matter knowledge, access to richly labeled data, as well as computational resources for model training and improvement.
Improving machine learning models requires an art that may be learned by systematically correcting the faults of the current model. In this post, I’ve outlined a variety of techniques for improving and updating models to achieve desired performance levels while minimizing data usage.