Stimulate Machine Learning With These 12 Expert Methods
June 16, 2021
6 min read
With the world digitizing rapidly ever since the pandemic, the field of machine learning has seen more reliance than ever before. Sectors such as healthcare, banking/insurance, aviation, oil and gas, and many others use this futuristic technology for better productivity and business outcome.
Speaking of outcomes, have you ever wondered about making changes in your existing predictive models to enhance the accuracy of the forecasted business results? If yes, then we have the best 12 expert methods to help you achieve this objective and help you minimize the trouble of getting your machine learning project off the ground. In other words, you will be able to perfect your machine learning algorithms and accelerate your business processes in a matter of days.
- Begin With Data Addition
The more data you add to the mix, the better your machine learning mechanism will be. It will also help you capture the different variants of these individual data sets. However, this method can only work on a case-by-case basis as we cannot quantify more data. It’s reliant on the project or problem you are trying to solve. For example, if you are working with time-series data, you should consider at least one year of data. On the other hand, when dealing with AI neural network algorithms, you should get more data for training the model; otherwise, it won’t generalize.
- Create Unsupervised Clusters
Cluster analysis finds a group of objects similar to each other in the group but unique from other groups. There are many clustering algorithms at present that have different characteristics. Clustering algorithms generally look at the metrics or the distance functions between feature vectors of the data points, and after that, they group the ones that are “near” each other. So it would be best if you made sure that these algorithms work best without the classes overlapping.
- Try Feature Engineering
When you add new features, it decreases skewing on the expense of variance of the model. These new features enable you to explain the variance of the model more potently. During hypothesis generation, there should be enough time spent on the components required for the model. The best way to do this is the creation of features from existing data sets.
- Utilize Automated Machine Learning
There was a time when the only way to find out the best model for your data was to train every possible model and see which performs best. Sometimes the best model might be an ensemble of others, which can be expensive to use for your interface. You might fail to realize that the best simple model can be nearly as good as the ensemble and cheaper to run. That’s where AutoML services aid you. It can help you find out the apt model, creates normalized and engineered feature sets, input missing values, drop correlated features, and even adds lagged columns for time-series forecasting.
- Distribute Your Data And Tune Your Parameters
You should explore data efficiently. However, data distribution may suggest transformation. You can apply algorithms with a bit of transformation to enhance predictability. You can also fine-tune the parameters of the algorithms for a given problem. The models have multiple parameters, and finding the best combination of parameters can help you search an issue. Tuning parameters improves the performance of the machine learning model if the right ones are used in an algorithm.
- Manage Missing Values
Having null values in your existing machine learning models is quite common. But the real fault lies in how you handle these minor discrepancies. You might feel that mean imputations are the best way to go about this problem, but we assure you that it would be a wrong approach. For example, if you are a machine learning startup dealing with a document with a client’s age and earnings and a 45-yr old prospect’s earning data goes missing. Then it will be wrong to assume an average earning value from an age range of 30 to 65 as it can vary from person to person based on their profession and designation.
Hence, we advise you to find out why the data went missing in the first place. After that, you can consider other methods of filling these gaps such as – feature prediction modeling, K nearest neighbor imputation, or even deleting the row.
- Boost Predictive Performance With Ensembles
When you use multiple machine learning models simultaneously, then it’s called ensemble learning. The primary purpose behind it is to better your predictive performance than an individual algorithm applied at one time. There are many ensembles that you can develop for your predictive modeling problem. However, three methods that have become very popular. These are – bagging, stacking, and boosting.
We advise you to have a detailed understanding of each of these methods to leverage them effectively. To describe in simple terms:
Bagging: Fitting multiple decision trees on different samples of the same dataset and then averaging the predictions.
Stacking: Fitting multiple varied models types on the same data and then using another model to combine the predictions best..
Boosting: Adding ensemble members sequentially to correct the predictions made by the prior models and create the weighted average of the predictions.
- Go ahead with transfer learning
When you reuse parts of a neural network trained for a similar application instead of training your neural network from scratch, it’s termed transfer learning. For example, consider image classification. First, you begin with a general image classification model; then, you keep contorted layers of the model. Finally, you only train the feedforward neural network on the top of these contorted layers via your datasets.
When you reuse parts of an existing model, you require selected updations, and you can gain higher frequency with a much smaller dataset. As a result, transfer deep learning can be a less brain wracking process in totality than any other approaches you have tried before.
- Avoid overfitting to save your model from being a failure
In simple terms, overfitting refers to a scenario where you train your datasets a little too well. A model may learn the detail and noise in the training data to such a degree that it negatively affects the performance of the model on new data. The noise or fluctuations that the training dataset has picked up and learned as concepts do not apply to the new data and, therefore, negatively impact the models’ ability to generalize.
Overfitting generally happens when the training datasets are small. We suggest trying approaches like L2 regularization, dropout, and batch normalization to avoid this issue. All of these are easy to implement within your model.
- Set up your data pipeline with utmost care
There are times when increasing computational power is not enough to decrease the training time. That means your data feeding pipeline might be at fault. Data pipelines are models of your ML process i.e. writing code, releasing to production, performing data extractions, creating training models, and tuning the algorithm. Many tutorials and pipelines are available on platforms like Azure machine learning, Amazon sagemaker, or TensorFlow Dataset to help you figure out how to make an efficient data pipeline.
It’s best to avoid the old data feeding pipelines and learn to create a proper data pipeline to increase the efficiency of your overall machine learning models.
- Fragment your code into smaller functions
It’s essential to follow good programming practices. When you begin an ML project, always consider preprocessing (reading, cleaning, and transforming data), computation, and lastly, post-processing (metamorphosis into the desired output format).
We believe if you keep these things in mind, then you can go a long way in terms of code clarity and reusability:
- Split all of your blocks into small tasks.
- Choose explicit and consistent function and variable names.
- Add many annotations.
- Use type hinting.
- Follow PEP 8 standards
- Modify hyperparameters to figure out your ideal model architecture
When you create your machine learning model, you will have many design choices to find out how you want to define your model architecture. However, most of the time, you might not know what the optimal model architecture would be for a particular model, and that’s why you need to explore a range of possibilities. The parameters which define the model architecture are called hyperparameters. The process of searching for the ideal model is called hyperparameter tuning.
When you adjust your existing ML model’s hyperparameters, you get a succinct idea about the ML model you are working on. Otherwise, it is a daunting task to understand each hyperparameter individually. There are different ways to achieve hyperparameter tunings, such as grid search, random search, or Bayesian optimization. It would help if you had a clear understanding of each of these before modifying or tuning your hyperparameters.
It’s not a cakewalk to train a machine learning model; it takes time and effort. The above methods have worked well for machine learning companies who have tried to perfect their existing business forecasting methodologies in our experience. It’s important to keep refining and retrain your current models as the data and concepts will keep drifting due to real-world events. You might even discover a whole different type of model which works better for the new data. So we encourage you to keep these techniques in mind and keep exploring the new possibilities.
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