How To Improve Machine Learning Model Performance: Five Ways

Machine learning development would be not difficult for ML engineers, but ensuring its performance is important to get accurate and most reliable results. Though, there are various methods you can improve your machine learning model performance.

Basically developed on python, machine learning models need to develop while considering the various factors that affect its performance. But right here we brought the list of most important parameters that you can consider while developing the ML model.

5 Ways to Improve Performance of ML Models

ways to improve performance of machine learning models

1. Choosing the Right Algorithms

Algorithms are the key factor used to train the ML models. The data feed into this that helps the model to learn from and predict with accurate results. Hence, choosing the right algorithm is important to ensure the performance of your machine learning model.

Also Read: How To Select Suitable Machine Learning Algorithm For A Problem Statement

Linear Regression, Logistic Regression, Decision Tree, SVM, Naive Bayes, kNN, K-Means, Random Forest and Dimensionality Reduction Algorithms and Gradient Boosting are the leading ML algorithms you can choose as per your ML model compatibility.

2. Use the Right Quantity of Data

The next important factor you can consider while developing a machine learning model is choosing the right quantity of data sets. And there are multirole factors and for deep learning-based ML models, a huge quantity of datasets is required for algorithms.

Also Read: How Much Training Data is Required for Machine Learning Algorithms

Depending on the complexities of problem and learning algorithms, model skill, data size evaluation and use of statistical heuristic rule are the leading factors determine the quantity and types of training data sets that help in improving the performance of the model.

3. Quality of Training Data Sets

Just like quantity, the quality of machine learning training data set is another key factor, you need to keep in mind while developing an ML model. If the quality of machine learning training data sets is not good or accurate your model will never give accurate results, affecting the overall performance of the model not suitable to use in real-life.

Also Read: How To Ensure Quality of Training Data for Your AI or Machine Learning Projects

Actually, there are different methods to measure the quality of the training data set. Standard quality-assurance methods and detailed for in-depth quality assessment are the leading two popular methods you can use to ensure the quality of data sets. Quality of data is important to get unbiased decisions from the ML models, so you need to make sure to use the right quality of training data sets to improve the performance of your ML model.

4. Supervised or Unsupervised ML

Moreover, the above-discussed ML algorithms, the performance of such AI-based models are affected by methods or process of machine learning. And supervised, unsupervised and reinforcement learning are the algorithm consist of a target/outcome variable (or dependent variable) which is to be predicted from a given set of predictors (independent variables).

In unsupervised machine learning, a model is given any target or outcome variable to predict/estimate. And, it is used for clustering population in different groups, which is widely used for segmenting customers in different groups for specific intervention. For supervised ML, labeled or annotated data is required, while for unsupervised ML the approach is different.

Also Read: Why Data Annotation is Important for Machine Learning and AI

Similarly, reinforcement Learning is another important algorithm, used to train the model to make specific decisions. In this training process, the machine learns from previous experiences and tries to store the best suitable knowledge for the right predictions.

5. Model Validation and Testing

Building a machine learning model is not enough to get the right predictions, as you have to check the accuracy and need to validate the same to ensure get the precise results. And validating the model will improve the performance of the ML model.

Also Read: How to Validate Machine Learning Models:ML Model Validation Methods

Actually, there are various types of validation techniques you can follow but you need to make sure choose the best one that is suitable for your ML model validation and help you to improve the overall performance of your ML model and predict in an unbiased manner. Similarly, testing of the model is also important to ensure its accuracy and performance.


Improving machine learning model performance will not only make the model predict in an unbiased manner but make it one of the most reliable and acceptable in the AI world. Hence, a machine learning engineer and data scientist need to make sure all these points while working on such models to improve the overall performance of the AI model.

Also Read: Why Data Annotation is Important for Machine Learning and AI

Anolytics, is one the leading data annotation company, provides the training data sets for machine learning models. You will get right here the best data sets for different types of machine learning models and improve their performance. Anolytics, is skilled in image annotation services to annotate the images and provide high-quality training data for computer vision-based AI models for different fields like healthcare, retail, automotive and agriculture, etc.

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