How To Improve Accuracy In Machine Learning Models

Hi there, this is Biraj, And in this blog post, I’m going to touch on some technical points about machine learning. So it’s been a long time since we wrote about machine learning. So it’s time for me to write about some technical steps about machine learning. Definitely, this post will help you to understand how to improve accuracy in machine learning

There are a lot of keywords in machine learning to speak, but accuracy is one of the essential things in machine learning to kickstart the conversation. Why the accuracy matrix is very important means increasing the accuracy of machine learning models will create a big impact in end implementations. That’s why many data scientists and machine learning engineers are putting more importance on increasing the accuracy of a machine learning model.

So what will happen if they increase the accuracy of machine learning models? The first change will be your outcomes. Suppose if you are implementing any machine learning algorithms in the automobile industry, accuracy will play a very important role in the automobile industry. Why I’m saying this, nowadays all the automobile industries are moving towards self-driving cars and considering that self-driving cars rely too much on machine learning accuracy.

Because in the concept of self-driving cars, the people are giving money that is based on the accuracy of each brand. If the car’s accuracy is good, then only people will buy that car. Otherwise, they don’t risk their life on self-driving cars. So this is where accuracy plays a vital role. And people’s thoughts are also like that.

They want safety and accuracy in doing that particular job. These two are very important for every end user. I think this example cleared up your thoughts about accuracy in machine learning. Let’s go to the content of how to increase the accuracy of machine learning models.

The first point is adding more data. Adding more data gives you more good and reliable results in the end implementations. By giving more variety of data to machine learning algorithms. you are adding more knowledge to the machine learning model. So it will get trained with various data circumstances, and it makes the model do some comparisons before giving an answer.

By adding more data, it will avoid making some biased decisions. And that means it makes super accurate decisions when it is deployed into the server or software. Suppose you have a limited number of data and you train your model with that data means, you cannot expect good results. At the end of the training, in the testing phase, your model will give you less accuracy and maybe you will get the results that are completely irrelevant to your expected results.

So this is why data plays a major role. And the more data you add, the more accurately we’ll get. This is the tough line in machine learning. Okay? The second one is a feature selection. The feature selection part is very important in machine learning. So in this part you need to select the features that will create an impact on your individuals adding more data is good But in the data, you have to select the features that will give less variance in your predictive model.

If your variance estimation is high, then you have to drop some features from the training part. By reducing the nonimpacted features in your data matrix, it will help you to reduce the variance and it is a more advisable step to drop some unwanted features from your dataset. And one more thing. If you select more relevant features and feed them into your model, you can avoid overfitting results too.

So in this step, I will give you two important methods to follow while selecting the features. The first one is the important feature. Some algorithms like Random Forest or XGBoost allow you to determine which features were the most important in predicting the target variable’s value. By quickly creating one of these models and conducting the feature importance, you will get an understanding of which variables are more useful than others. View In Audio AI

And the second one is the dimensionality reduction. One of the most common dimensionality reduction techniques is the PCA. PCA means principal component analysis. It takes a larger number of features and uses linear algebra techniques to reduce them into fewer features. And the third point is hyperparameter tuning.

Training a machine learning model is a skill that will come if you practice more in problem-solving. And sometimes these above techniques are not enough to improve the accuracy of your model. So in the field of machine learning, there are a lot of techniques you have to follow to increase the accuracy of the machine learning model.

You can’t stick with a particular technique to improve the accuracy of a machine-learning model. One of the best ways to increase the accuracy of a machine-learning model is by implementing hyperparameter tuning in your model. So it is like a trial and error method. You have to change the model is a strategy that is followed. The hyperparameter tuning. Hyperparameter tuning is more important in unsupervised learning. So take an example when you are training the k means algorithm and you don’t know how many clusters to add. In this case, you have to change the cluster values until you get some good accuracy.

And the fourth one is the ensemble method. This is one of the most common methods which is used by every data scientist. Suppose if you are hearing the ensemble words for the first time, this is the explanation about the ensemble methods.

Ensembl methods are nothing but combining the multiple trained models and producing good accuracy at the end. So this is the process of Ensembl methods. The Ensembl method is a winning strategy for every data scientist and machine learning engineer. This can be achieved through two common methods. The first one is the bagging and the second one is the boosting.

And the final one is the outlayers detection and anomalies. Outlayers and anomalies are the same.

So the outlayer term explains that every data point that is present in the dataset has some relations between each other. If some data points are unlike each other, those points are called outlayers. So outlayers will reduce the accuracy of the model that you trained. So if you find any outlayers, you have to remove that first in your dataset.

The process of removing outlayers is called outlayer detection. Or anomaly detection. So why do the outliers in the machine learning model mean that data leads to data corruption and it affects your accuracy also? There are some methods and algorithms are there for identifying the outliers in the data set that are the standard deviation and interquartile range methods.

So these two algorithms are very useful to remove the outliers from the dataset. So we are in the final part of this video. These five methods are very important to increase the accuracy of any machine learning model. But there are a lot of methods to increase the accuracy other than this, like cross-validation and so many methods are there to increase the accuracy.

So we will cover those topics in the coming weeks. If you like this type of post, then support this by sharing and subscribing to our newsletter, it will help motivate me and it will bring you more articles and posts from my side. And that’s all about this post.

Thanks for reading till here. Written by Audio AI