Training data is the key input to machine learning (ML) and having the right quality and quantity of data sets is important to get accurate results. The larger the training data available for the ML algorithm, it will help the model to perceive the diverse types of objects making it easier to recognize them when used in real-life predictions.

But the question here is, how will you decide how much training is enough for your machine learning. As insufficient data will affect your model prediction accuracy while more than enough data can give the best results, but can you manage the big data or huge quantity of datasets and it also required deep learning or a more complex way to feed such data into algorithms.

Many factors decide how much training data is required for machine learning like your model complexity, **machine learning algorithms **and data training or validation process. And in some cases, how much data is required to demonstrate that one model is better than another. All these factors considered while choosing the right number of datasets let us discuss more elaborately to find how much data is enough of ML.

## Depends on the Complexities of Problem and Learning Algorithms

One of the most important factors while selecting** training data for machine learning **is the complexity of the problem means the unknown underlying function that relates to your variable inputs to the output variable as per the ML model type.

**Also Read**: **What are the various Types of Data Sets used in Machine Learning?**

Similarly, the complexity of the machine learning model algorithm is another important factor considered while choosing the right quantity of data sets. The algorithm is used to inductively learn the unknown underlying mapping function from specific examples to make the best use of training data and integrate the same into the machine learning model.

## Using the Statistical Heuristic Rule

In statistical terms, there are many components considered like a factor of the number of classes, a factor of the number of input features, and a factor of the number of model parameters. And there are statistical heuristic methods available that allow you to calculate suitable sample size.

In the factor of the number of classes, there must be X independent examples for each class, where x could be tens, hundreds, or thousands depending upon your parameter range. While input features there must be X% more examples than their input features and in model parameters, there must be independent examples for each parameter in the model.

### Model Skill vs Data Size Evaluation

While choosing the **training data set for machine learning** you can design s study that can evaluate the model skill required against the size of the training dataset. To perform this study, plot the result of your model prediction, as a line plot with training dataset size on the x-axis and model skill on the y-axis that will give you an idea of how much the quantity of data affects the skill of the model while solving a specific problem with machine learning.

You can use a learning curve in which you will be able to project the amount of data required to develop a skillful model or perhaps how small data you needed before touching an inflection point of diminishing returns. So, you can perform the study with available data and single performing algorithms like **random forest** and suggest you develop robust models in the context of a well-rounded understanding of the problems.

### More Data Required for Nonlinear Algorithms

Nonlinear algorithms are usually known as one the most powerful machine learning algorithms. As they are capable to learn the complex nonlinear relationships between inputs and output features. If you are using nonlinear algorithms you need an adequate amount of data sets and need to **hire a machine learning engineer** that can work with such applied mathematics.

**Also Read :** **How to Create Training Data for Machine Learning**?

Such algorithms are often more flexible and even nonparametric means they can find out itself how many parameters are required to model your problem in addition to the values of those parameters. The predictions with such models vary based on the particular data used to train them resulting in lots of data required for such model training.

#### Don’t Wait for More Data, Get Started what you have

You don’t need to get a sufficient amount of training data for your ML and waiting to acquire such data for long days is not a sensible decision. Don’t let the problem of the training set size stop you from getting started on your model prediction problem-solving.

Get started with the data you can, use what you have, and check how effective models are on your problem. Acquire something then take action to understand better what you have with for further analysis and then increase the data you have with augmentation or collect more data from your domain to make your model training more accurate.

#### Conclusion

The quality and quantity of training data are some of the most important factors machine learning engineers or data scientists are taking into serious consideration while developing a model. However, in coming years it would become more clear how much training data is sufficient for machine learning model development but it is clear now **“the more the better”**. Hence, if you can acquire as much data and utilize the information it would be better for you, but waiting for bid data acquisition for a long time can delay your projects.

**Also Read** : **How To Hire A Good Data Scientist: Five Easy Steps**

**Cogito **is one of the companies providing high-quality training data sets for machine learning and AI. It is involved in data collection, classification, and categorization with **image annotation services** to provide well-supervised training data at an affordable cost. It is providing the training data for all leading sectors including medical, automobile, agriculture and retail sectors ready to adopt the machine learning or AI-based automated systems.