There are multiple ways to boost your predictive model accuracy. Most of these steps are really easy to implement, but yet for many reasons data scientist fail to do proper data preparation and model tuning. in the end, they end up with average or below average machine learning models.
Here I try to outline the easiest steps that will improve your machine learning model.
Data Cleansing is one of the most important procedures for improving your model performance.
Most times when we collect data, we must do data cleaning, to ensure that the data is as perfect as it can be. Data cleaning can involve many assessments. For example, let’s say a survey questionnaire was put online and data was collected via a website.
What are the best ways to clean your data?
Data scientists face tons of metrics when we use machine learning. We don’t really need to know the tiny details of every one to make our machine learning models shine.
We just need to know the most important metrics to be sure that our machine learning models are performing to their best using our data.
What is Cohen’s Kappa?
Factor analysis is a technique to reduce the number of attributes when the relationships between those attributes are not that obvious. Essentially, Factor analysis analyzes interrelationships (or correlations) among a large number of items and reduces the large number of these items into smaller sets of factors. This smaller set of factors can then be used in further analysis — e.g., in logistics regression or neural network to predict your outcome.
Work with Factor Analysis