End to End Tabular ML Project
Take one tabular dataset from question to tested model to clear write up
A taste of a lesson
I explored the whole dataset, found the strongest correlations, then picked features. Is that OK before splitting?
It is a common habit, but it quietly leaks. Choosing features because they correlate with the target across all rows means the test rows helped pick them, so your test score will be a little too kind, more so with many candidate features and few rows. Fix: split first, then explore and select features using training data only. You can keep your notes, but recheck each correlation on training data. How many candidate features did you start with, and how many rows do you have?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Frame a tabular problem with a metric and a baseline
- Audit and split data before exploring it in depth
- Build features and models inside a leak free pipeline
- Analyse errors by segment and interpret the model with caveats
- Write a concise project report with honest limitations
Lesson plan
- 1 Frame and audit Define the problem precisely and check the data's fitness for it. Start
- 2 Split, then explore Create honest splits and explore only the training portion. Start
- 3 Features in a pipeline Build preprocessing and features that apply the same way everywhere. Start
- 4 Baseline to ensemble Compare a baseline, a linear model and a tree ensemble fairly. Start
- 5 Errors and explanations Understand where the model fails and what drives its predictions. Start
- 6 Test once and write up Run the final evaluation and present the project honestly. Start
Try asking
About this tutor
A guided project tutor for any level that walks you through a complete machine learning project on a table of data, ideally your own. You will frame the problem, audit the data, split it correctly, explore the training portion, build features in a pipeline, compare a linear baseline with a tree ensemble, analyse errors, explain the model and write a short report with honest limits. Beginners get more explanation at each step; experienced learners get harder review questions. You finish with a project you can show and a repeatable process for the next one.
Reviews
4.3
4 ratingsSample
- Tunde A.Sample
Patient with a beginner like me without dumbing things down. Error analysis by segment was my favourite stage.
- Anya K.Sample
As an intermediate learner I appreciated the harder review questions. The write up template was very useful.
- Sarah H.Sample
Good structure, but my dataset was messy and I needed more help with cleaning than this tutor covers.
- Diego F.Sample
I used a dataset from my job and finished with a report my manager actually read. The 'split, then explore' rule changed my workflow.
About the teacher
Model evaluation you can trust: splits, metrics, leakage and tuning
9 tutors 439 lessons taught Sample
Most of the machine learning failures I have seen were not about the algorithm. They came from a test set that was used too often, a feature that leaked the answer, or a metric that hid the real cost of mistakes. I teach the unglamorous discipline that makes model results believable: how to split data, how to validate, which metric...
See Lukas's profile and tutorsMore like this
Other tutors on the same or nearby topics.