Collaborating Authors

Hyperparameter Tuning with Grid Search and Random Search


Hyperparameters are parameters that are defined before training to specify how we want model training to happen. We have full control over hyperparameter settings and by doing that we control the learning process. It can be set to any integer value but of course, setting it to 10 or 1000 changes the learning process significantly. Parameters, on the other hand, are found during the training. We have no control over parameter values as they are the result of model training.

Hyperparameter Optimization Techniques to Improve Your Machine Learning Model's Performance


When working on a machine learning project, you need to follow a series of steps until you reach your goal. One of the steps you have to perform is hyperparameter optimization on your selected model. This task always comes after the model selection process where you choose the model that is performing better than other models. Before I define hyperparameter optimization, you need to understand what a hyperparameter is. In short, hyperparameters are different parameter values that are used to control the learning process and have a significant effect on the performance of machine learning models. These parameters are tunable and can directly affect how well a model trains. So then hyperparameter optimization is the process of finding the right combination of hyperparameter values to achieve maximum performance on the data in a reasonable amount of time.

Hyperparameter Optimization Techniques for Data Science Hackathons


For the python code, I used the Iris dataset which is available within the Scikit-learn package. It is a very small dataset (150 rows only) with a multiclass classification problem. As we are mostly focussing on hyperparameter tuning, I have not performed the EDA(exploratory data analysis) or feature engineering part and directly jumped into the model-building. I used the XGBoostClssifier algorithm for the model-building to classify the target variables. Then, we pass predefined values for hyperparameters to the GridSearchCV function.

Implementing Custom GridSearchCV and RandomSearchCV without scikit-learn


Grid Search can be thought of as an exhaustive search for selecting a model. In Grid Search, the data scientist sets up a grid of hyperparameter values and for each combination, trains a model and scores on the testing data. In this approach, every combination of hyperparameter values is tried which can be very inefficient. For example, searching 20 different parameter values for each of 4 parameters will require 160,000 trials of cross-validation. This equates to 1,600,000 model fits and 1,600,000 predictions if 10-fold cross validation is used.

Sentiment Analysis


Sentiment Analysis, as the name suggests, it means to identify the view or emotion behind a situation. It basically means to analyze and find the emotion or intent behind a piece of text or speech or any mode of communication. In this article, we will focus on the sentiment analysis of text data. We, humans, communicate with each other in a variety of languages, and any language is just a mediator or a way in which we try to express ourselves. And, whatever we say has a sentiment associated with it.