What Is Accuracy in the Machine Learning (ML) Model?
The efficacy of any data science project completely relies upon the accuracy of the machine learning model. Although it sounds simple, maintaining a higher accuracy of the ML model is not that straightforward.
Most Data scientists and AI experts struggle a lot to attain the highest accuracy in a machine learning model. However, by properly arranging the ML model-building processes, the model can quite easily achieve the highest possible accuracy.
Here we will be discussing the steps through which you will certainly get the answer to your query, 'How to increase the accuracy of a machine learning model?'
Machine learning model accuracy- What does it mean?
Machine learning models are helpful for businesses to make better decisions. The more accurate a model is, the better results it will produce. The accuracy of a machine learning model determines a high probability of finding patterns and relationships among dataset values.
An accurate model can understand unseen data to generate better predictions and insights. So businesses can benefit more from the process by preventing substantial time and money resources.
Web scraping can be an alternative method to enhance and increase the efficiency of machine learning models.
What is web scraping?
Retrieving data from internet sources like websites is known as web scraping. Artificial intelligence automation can be used to gain thousands or millions of data sets in less duration and can then be restructured.
Predictive modeling can utilize web scraping tools to obtain missing data so that they can function properly. As predictive modeling requires massive amounts of data, extracting online data is a viable option.
Another area is NLP (Natural Language Processing), where web crawlers can automatically analyze online data to obtain regularly updated information. NLP systems benefit from this process because they do not have to rely on manual updation every time they analyze online data generated by humans.
Tips to improve the machine learning model accuracy
1. Add clean and relevant data
For instance, if you want a student to gain higher grades in the examination, you must provide the student with better study materials before the inspection. It is the same with the machine learning model, the ML model needs to be trained as a student, and the study material is the data. So you need to provide the model with better data so it can be trained properly on the said data.
The sample size must also be increased by locating data sources (open source). Whenever possible, try to match your data with new data and entries. For example: if a machine learning model is trained on fewer data, then the model will not be able to recognize and differentiate between the objects in a picture.
2. Handling missing data and outliers
When you collect data from multiple resources, sometimes the data is not clean, which means it has missing values and outliers. It is vital to work on missing values and outliers. This will give you low accuracy, or you may get biased accuracy.
How to handle missing value?
- Remove the column that has a missing value.
If a particular column has a missing value, then deleting the column is one option, but it is not that effective. I.e., If much of the information is discarded, it's impossible to complete a reliable analysis.
- Mean/median/mode imputation.
Any missing value in a given column is replaced with the column's mean (median/mode).
- Regression Implementation
The method replaces the missing value with the predicted value based on the regression line.
How to handle Outliers?
It is the value that deviates from the other values. These values appear as a result of measurement or execution errors. So, remove the "noise data."
3. Try Multiple Algorithms
The best approach how to increase the accuracy of the machine learning model is opting for the correct machine learning algorithm. Choosing a suitable machine learning algorithm is not as easy as it seems. It needs experience working with algorithms.
The most suitable model can be chosen based on the dataset. Suppose there is an algorithm, A1 and you have two datasets named, D1 and D2, respectively. Now, algorithm A1 works the best with dataset D1 but might not offer good accuracy when applied to dataset D2. So you have to use all relevant modes and check the performance accordingly.
(Hint: If your data is linear, linear regression may work. If tuning parameters is essential, you can choose a machine learning algorithm like SVM, which can tune the parameters. The neural network has more convergence time, and a random forest needs more time to train the data.)
The critical purpose of cross-validation is to check how the model will perform on unknown data. It is a model evaluation and training technique that splits the data into several parts. The idea is to change the training and test data on every iteration. Cross-validation is the most popular solution to the queries, 'How to increase the accuracy of machine learning models?'
Effective tool for training models with smaller datasets:-
Leave one out of cross-validation (LOOCV)
Stratified K-fold cross-validation.
Leave p-out cross-validation.
5. Hyper-parameter tuning
The performance of an algorithm in machine learning is driven by its parameters. We can change the value of parameters accordingly when needed. To improve machine learning models, parameter tuning is used to find the value for every parameter. Tuning basically indicates changing the parameter value.
When tuning these parameters, a great understanding of the parameters and the personal impact on the model is needed to keep repeating this process with different well-performing models.
(Note: We have parameters like min_sample_split, min_sample_leaf, max_depth of a tree, max_leaf nodes. The initial optimization of these values will result in more and better accurate models.)
6. Dimensionality Reduction
First, Let us know what the dimensions are.
Dimension, actually known as features, means input features, variables, and columns present in a given dataset. Some dataset contains many parameters leading to complex predictive modeling tasks. So, visualization and prediction become difficult if features are in large numbers, so reducing the features will help you gain more accuracy in machine learning models.
This process of reducing high dimensional space (high dimensional data) to low dimensional space is called Dimensionality Reduction (DR). In common words, many attributes are reduced to fewer features but without any loss of information.
The dimensionality reduction technique solves classification and regression issues like
Methods to reduce the dimension of training data:
Backward/forward feature selection and others
7. Train with a different algorithm
For example, let us assume you want to invest in a company or industry. Will you seek one person's advice or take advice from different industry experts? If you want to gain more accuracy, consider taking other people's directions before the next step.
Accordingly, when you want to obtain accurate results in machine learning, you can try to imply different machine learning models (called weak learners) and later average the accuracies to get the final result. So, different algorithms will help in improving machine learning models.
Apply ensemble technique
Combining the result of multiple weak machine learning models is known as ensemble learning. It can be achieved by Bagging and Boosting.
Bagging (Bootstrap aggregating)
We will give the different subsets of the dataset for other models, and the data in different subsets can also be repeated.
Every subset is trained in different models. The models' outputs are combined in average (in regression problem) or voting (in classification problem) in a single production.
When To Use Bagging?
It is only effective in the case of unstable nonlinear models. (i.e., a slight change in the training set can cause a drastic change in the models)
When to Use Boosting?
Assigning weights depends on the 'correct classification' and vice versa. It is an iterative technique. In this, the importance of the observation is adjusted based on the last classification.
If you want to know more about Bagging and Boosting, read the following blog:
Fundamentals of Bagging and Boosting in Machine Learning | Ensemble Method
Points to remember while implementing machine learning algorithms:-
To enhance prediction accuracy, building, and testing hypotheses is mandatory.
Data should be cleaned and preprocessed to account for missing and outlier values.
Generate unique characteristics from available data using feature selection.
Try multiple model selection processes to get the best model for your data.
Tune hyperparameters to improve model performance.
For improved performance, use ensemble methods to merge different models.
Try understanding the proper steps to create an accurate ML model, and the process will become easier. Your coding knowledge and algorithmic problem-solving skills are the ultimate keys to enhancing the Machine learning models.
So, it's now clear that you can improve the accuracy of the machine learning model through various methods and approaches. The above-mentioned methods, such as hyperparameter tuning, feature selection, and using different algorithms, can really enhance the accuracy of the ML model.
So far, we have seen various ways how to increase the accuracy of machine learning models. The best approach is to keep practicing to improve your understanding of the data and algorithms. Your model will perform better if you keep learning from your mistakes and improving.
The process of creating an accurate model can be difficult and tedious. To be efficient in ML model creation, you to strengthen the base, which is possible through an excellent Artificial Intelligence and Machine Learning Program that teaches the fundamentals and helps in practical implementation.
You will be ahead of the curve by learning under the guidance of expert mentors. Once you gain the basic knowledge, you can involve your expertise within real-world applications.