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𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
How do you handle SVM's bias-variance tradeoff?

Tuning the SVM’s 𝗖 and 𝗴𝗮𝗺𝗺𝗮 parameters plays a crucial role in managing the model's bias-variance tradeoff, directly influencing the model's complexity, generalizability, and how well it can handle unseen data.

𝗧𝗵𝗲 𝗖 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿
Effect on Margins: C controls the penalty for misclassified points. A high C forces the model to classify training points more accurately, potentially reducing the margin and creating a more complex decision boundary that fits the training data closely. This reduces bias but increases variance, risking overfitting.

High C: Low bias (since the model tries to perfectly classify the training data) but high variance (overfitting).
Low C: High bias (since the model allows more misclassifications, resulting in a larger margin) but low variance (underfitting).

𝗧𝗵𝗲 𝗴𝗮𝗺𝗺𝗮 𝗣𝗮𝗿𝗮𝗺𝗲𝘁𝗲𝗿 (𝗳𝗼𝗿 𝗡𝗼𝗻-𝗹𝗶𝗻𝗲𝗮𝗿 𝗞𝗲𝗿𝗻𝗲𝗹𝘀)
Effect on Feature Space: gamma determines the influence of each training point in the decision boundary by controlling the scale of the kernel function. A high gamma restricts influence to points very close to the decision boundary, creating more complex, localized boundaries. This can lead to high variance and overfitting.

High gamma: Low bias, high variance (overfitting) as the model can create extremely localized, intricate boundaries.
Low gamma: High bias, low variance (underfitting) as the model forms smoother, simpler decision boundaries.

Essential Topics to Master Data Science Interviews: 🚀

SQL:
1. Foundations
- Craft SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING
- Embrace Basic JOINS (INNER, LEFT, RIGHT, FULL)
- Navigate through simple databases and tables

2. Intermediate SQL
- Utilize Aggregate functions (COUNT, SUM, AVG, MAX, MIN)
- Embrace Subqueries and nested queries
- Master Common Table Expressions (WITH clause)
- Implement CASE statements for logical queries

3. Advanced SQL
- Explore Advanced JOIN techniques (self-join, non-equi join)
- Dive into Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)
- Optimize queries with indexing
- Execute Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Python Basics
- Grasp Syntax, variables, and data types
- Command Control structures (if-else, for and while loops)
- Understand Basic data structures (lists, dictionaries, sets, tuples)
- Master Functions, lambda functions, and error handling (try-except)
- Explore Modules and packages

2. Pandas & Numpy
- Create and manipulate DataFrames and Series
- Perfect Indexing, selecting, and filtering data
- Handle missing data (fillna, dropna)
- Aggregate data with groupby, summarizing data
- Merge, join, and concatenate datasets

3. Data Visualization with Python
- Plot with Matplotlib (line plots, bar plots, histograms)
- Visualize with Seaborn (scatter plots, box plots, pair plots)
- Customize plots (sizes, labels, legends, color palettes)
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Excel Essentials
- Conduct Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)
- Dive into charts and basic data visualization
- Sort and filter data, use Conditional formatting

2. Intermediate Excel
- Master Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)
- Leverage PivotTables and PivotCharts for summarizing data
- Utilize data validation tools
- Employ What-if analysis tools (Data Tables, Goal Seek)

3. Advanced Excel
- Harness Array formulas and advanced functions
- Dive into Data Model & Power Pivot
- Explore Advanced Filter, Slicers, and Timelines in Pivot Tables
- Create dynamic charts and interactive dashboards

Power BI:
1. Data Modeling in Power BI
- Import data from various sources
- Establish and manage relationships between datasets
- Grasp Data modeling basics (star schema, snowflake schema)

2. Data Transformation in Power BI
- Use Power Query for data cleaning and transformation
- Apply advanced data shaping techniques
- Create Calculated columns and measures using DAX

3. Data Visualization and Reporting in Power BI
- Craft interactive reports and dashboards
- Utilize Visualizations (bar, line, pie charts, maps)
- Publish and share reports, schedule data refreshes

Statistics Fundamentals:
- Mean, Median, Mode
- Standard Deviation, Variance
- Probability Distributions, Hypothesis Testing
- P-values, Confidence Intervals
- Correlation, Simple Linear Regression
- Normal Distribution, Binomial Distribution, Poisson Distribution.

Show some ❤️ if you're ready to elevate your data science game! 📊

ENJOY LEARNING 👍👍

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻:
How would you extend SVM for multi-class classification?

Two common ways are -

𝗢𝗻𝗲-𝘃𝘀-𝗥𝗲𝘀𝘁 (𝗢𝘃𝗥) (𝗼𝗿 𝗢𝗻𝗲-𝘃𝘀-𝗔𝗹𝗹)
Each classifier is trained to separate one class from all others. For K classes, OvR builds K SVM models, where each model is trained with the class of interest labeled as positive and all other classes labeled as negative. For a new instance, each classifier outputs a score, and the class with the highest score is chosen as the predicted class.

Pros of OvR -
🧤 Computationally efficient, especially when there are many classes, as it requires fewer classifiers.
🧤 Works well when the dataset is large, and class overlap isn’t significant.

Cons of OvR -
🔻 The negative class for each classifier can be a mix of very different classes, which can make the boundary between classes less distinct.
🔻 May struggle with overlapping classes, as it requires each classifier to make broad distinctions between one class and all others.

𝗢𝗻𝗲-𝘃𝘀-𝗢𝗻𝗲 (𝗢𝘃𝗢)
This method involves building a separate binary classifier for each pair of classes, resulting in (K(K−1))/2 classifiers for K classes. Each classifier learns to distinguish between just two classes. For classification, each binary classifier votes for a class, and the class with the most votes is selected.

Pros of OvO -
🧤 Creates simpler decision boundaries, as each classifier only has to separate two classes.
🧤 Often yields higher accuracy for complex, overlapping classes since it doesn't force each classifier to distinguish between all classes.

Cons of OvO -
🔻 Computationally intensive for large numbers of classes, due to the higher number of classifiers.
🔻 Prediction time can be slower as it requires voting among all classifiers, which can be significant if there are many classes.

𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝗢𝘃𝗥 𝗮𝗻𝗱 𝗢𝘃𝗢
The choice between OvR and OvO depends largely on the specific dataset characteristics and computational constraints:
👉 If computational resources are limited and the number of classes is high, OvR may be preferred, as it requires fewer classifiers and is faster to train and predict with.
👉 If accuracy is critical and the classes overlap significantly, OvO often performs better since it learns more specialized decision boundaries for each pair of classes.

So what should an entry-level interview experience look like?

Having been on both sides of the process - this format, IMO, is the most effective one

Round 1:
⭐️ 30 minutes LeetCode, 30 minutes SQL
The goal? Understand how candidate approaches the problem - clarifies ambiguity, addresses edge cases, and writes code.
Passing a few test cases is required, but not all.
Better than brute force is required, optimal solution is not.

Round 2:
⭐️ Machine Learning/Statistics and Resume-based
The goal? Make sure they understand basic concepts - bias vs variance, hypothesis testing, cleaning data etc. and how they have approached ML formulation, metric selection and modelling in the past.

Round 3:
⭐️ Hiring Manager (+ senior team member) to review work on resume + culture fit
The goal? For the HM and senior team members to assess if the candidate is a culture fit with the team; To review prior work and see if how they think about solving a data/ML problem would work in the team (or if the person is coachable)
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