GrokkingDataScientist.tar.gz

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Master the skills that can get you a $100K+ salary even if you bunked your statistics classes. 

No need to waste hours and hours on browsing from one article to the next and piecing together the info you need to grasp important topics. No need to get overwhelmed by the information overload. Find easy to follow, hands-on, and fun explanations of all the essential topics in one place so you can quickly and efficiently learn what you need to thrive as a data scientist.

"Is this course right for me?" Continue to read to decide for yourself!

- "I want to understand this data science concept. Let me Google it". Then after hours of surfing, reading random articles, and invoking the heavens, you are more confused than before.
- "Data science is the sexiest and highest paying job of the 21st century. I want to become a data scientist too".
- "I have a basic knowledge of Python, willingness to learn, and commitment to become a great data scientist."

Is that you? If yes, you are at the right place.

Grokking Data Science

## 5. Fine-Tune Your Model 

Say our best performing model was the RandomForestRegressor. This is a model that has many input hyperparameters that can be tweaked for improving performance. For example, we could have a forest with 100 or 1000 trees, or we could use 10 or 50 features during random selection. What are the best values for these hyperparameters to pass as input to the model for training? 

### Grid Search

Should we fiddle with all the possible values manually and then compare results to find the best combination of hyperparameters? This would be really tedious work, and we would end up exploring only a few possible combinations.

Luckily, we can use Scikit-learn’s `GridSearchCV` to do this tedious search work for us. All we need to do is tell it which hyperparameters we would like to explore and which values to try out, and it will evaluate all the possible combinations of hyperparameter values, using cross-validation. 

For example, let's see how to search for the best combination of hyperparameter values for the RandomForestRegressor: 

# 5. Fine-Tune Your Model 

Say our best performing model was the RandomForestRegressor. This is a model that has many input hyperparameters that can be tweaked for improving performance. For example, we could have a forest with 100 or 1000 trees, or we could use 10 or 50 features during random selection. What are the best values for these hyperparameters to pass as input to the model for training? 

## Grid Search

Should we fiddle with all the possible values manually and then compare results to find the best combination of hyperparameters? This would be really tedious work, and we would end up exploring only a few possible combinations.

Luckily, we can use Scikit-learn’s `GridSearchCV` to do this tedious search work for us. All we need to do is tell it which hyperparameters we would like to explore and which values to try out, and it will evaluate all the possible combinations of hyperparameter values, using cross-validation. 

For example, let's see how to search for the best combination of hyperparameter values for the RandomForestRegressor: 

Python Fundamentals for Data Science

The Fundamentals of Statistics

Machine Learning 101

End-to-End Machine Learning Project

The Real Talk

Kaggle Challenge - Fine Tune Parameters

5. Fine-Tune Your Model

Grid Search