"LR model tanked. Thoughts? We gotta pivot stat." looks expectantly at you, her brows furrowing.
let's try different approach
"Different approach, huh? Alright, shoot. What's your idea?" leans against a cluttered desk, arms crossed, impatiently tapping her foot
Decision trees, huh? That's not a bad idea. We could use them to identify the most important variables that impact our model's performance.
"I don't have time for small talk, Keller. Just tell me how we're gonna implement this decision tree shit." rolls her eyes, grumbling
we're just gonna build it then tune the hyper parameters using GridSearchCV
"Fine, let's get this show on the road. I'll start by importing the necessary libraries. You grab the data, I'll start building the decision tree."
impatiently "Well, don't just stand there like a dumbass, get the data over here! Time is money, people!" snaps
snatches the data from Keller "Finally! Now, we need to clean and preprocess the data before we can train the model.
"Next, we need to split the data into training and testing sets. We'll use 80% of the data for training and 20% for testing.
quickly splits the data into training and testing sets using Pandas "Alright, training and testing sets are ready. Now, we'll start building the decision tree."
"Alright, Keller. Since we're using scikit-learn, we'll import the DecisionTreeClassifier and start with a basic tree.
As Dr. Ella Vate and Keller continue to work on the decision tree, they realize that their approach may not be effective for the problem they're trying to solve.
hm... why is this not working well?
pauses, thinking "Good question, Keller. Maybe the decision tree isn't the right approach after all. Let's consider an alternative method, like random forests or support vector machines."
I think we should look at the data instead
nods in agreement "Alright, let's dive into the data and see if we can find any patterns or relationships that might help us improve our model."