Main Challenges faced while Solving Machine Learning Problems
career in machine learning can bring job satisfaction, great growth, and high pay, but it’s a complex and challenging process. While many researchers and experts agree that we are in the golden age of artificial intelligence, there are still many hurdles and challenges to overcome when developing projects. With all of this in mind, let’s take a look at some of the hurdles companies face in developing machine learning technology.
9 Challenges of Machine Learning are:
1) Insufficient Quantity Of Training Data
Machine Learning takes a lot of data for most Machine Learning algorithms to work properly. Even for simple problems you typically need thousands of examples, and for complex problems such as image or speech recognition, you may need millions of examples.
2) Nonrepresentative Training Data
To generalize well, your training data must be representative of the new case you want to generalize to. This is true whether you use instance-based learning or model-based learning.
By using a nonrepresentative training set, we trained a model that is unlikely to make accurate predictions, especially for very poor and very rich countries.
It is crucial to use a training set that is representative of the case you want to generalize to. This is often harder than it sounds: if the sample is too small, you will have sampling noise, but even very large samples can be nonrepresentative if the sampling method is flawed. This is called sampling Bias.
3) Poor-Quality Data
If your training data is full of errors, outliers, and noise, it will make it harder for the system to detect the underlying patterns, so your system is less likely to perform well. It is often well worth spending time cleaning up your training data. The truth is, most data scientists spend a significant part of their time doing just that.
4) Irrelevant Features
Your system will only be capable of learning if the training data contains enough relevant features and not too many irrelevant ones. A critical part of the success of a Machine learning project is coming up with a good set of features to train on.
This process, called feature engineering, involves the following steps:
- Feature Selection: Select the most useful existing features to train on.
- Feature Extraction: Combining existing features to produce a more useful one.
- New Features: Creating new features by gathering new data.
5) Overfitting The Training Data
In Machine Learning there is something called Overfitting. It means that the model performs well on the training data, but it does not generalize well.
Complex models such as deep neural networks can detect subtle patterns in the data, but if the training set is noisy, or if it is too small, then the model is likely to detect patterns in the noise itself.
Overfitting Happens when the model is too complex relative to the amount and noisiness of the training data. Here are some possible solutions:
- Simplify the model by selecting one with fewer parameters, by reducing the number of attributes in the training data, or by constraining the model.
- Gather more training data.
- Reduce the noise in the training data(by fixing errors and remove outliers).
6) Underfitting The Training Data
Underfitting is the opposite of Overfitting: It occurs when your model is too simple to learn the underlying structure of the data.
Example: A linear model of life satisfaction is prone to underfit; Reality is just more complex than the model, so its predictions are bound to be inaccurate, even on the training data.
How to fix Underfitting Problem:
- Select more powerful model, model with more parameters.
- Feed better features to the learning model.
- Reduce the Constraints(Regularization Parameter) on the model.
7) Testing and Validating
The only one way to know how well a model will generalize to new case is to actually try it out on new case.
So there is the option to split your data into two sets: the training set and test set. As these names imply, you train your model using the training set, and you test it using the test set. The error rate on new cases is called the generalization error, and by evaluating your model on the test set, you get an estimate of this error. This value tells how well your model will perform on instances it has never seen before.
8) Hyperparameters Tuning and Model Selection
A Machine Learning model is defined as a mathematical model with a number of parameters that need to be learned from the data. By training a model with existing data, we are able to fit the model parameters.
However, there is another kind of parameter, known as Hyperparameters, that cannot be directly learned from the regular training process. They are usually fixed before the actual training process begins. These parameters express important properties of the model such as its complexity or how fast it should learn.
Some examples of model hyperparameters include:
- The penalty in Logistic Regression Classifier i.e. L1 or L2 regularization
- The learning rate for training a neural network.
- The C and sigma hyperparameters for support vector machines.
- The k in k-nearest neighbors.
9) Data Mismatched
In some cases, it’s easy to get a large amount of data for training, but this data probably won’t be perfectly representative of the data that will be used in production.
Example: If you want to create a mobile app to take pictures of flowers and automatically determine their species. You can easily download millions of pictures of flowers on the web, but they won’t be perfectly representative of the images that will be taken using the app on a mobile device.
In this case, the most important rule to remember is that the validation set and the test set must be representative as possible of the data you expect to use in production, so they should be composed exclusively of representative pictures.
Outgo and Resources for further Experiment.
If you Liked This Article and you have some doubt and you want the Brief Explanation then please Consider Checking The Book called Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow.
Follow Me
If you find the ideas I share with you interesting, please don’t hesitate to connect here on My Social Profile.
