curse of Dimensionality
case 1) few feature
  1. Attendance ( % )
  2. Internal Marks
  3. Assignment Marks
case 2; Too many feature
  1. AH
    • IM
    • Assignment
    • Lab marks
    • project score
overcome the curse of dimensionality
  1. feature selection
  2. feature Extraction
  3. collect more training Data
  4. Regularization -> unwanted
  5. use Domain knowledge.
underfiting ; few data
overfiting ; To many data
Datasets
Training Validation Testing
Bias and Variance
plotting s data
  • scatter plots
  • Bar plot
  • Histreogram
  • line plot.
Plotting Libraries
  • Matplotlib
  • pandas.
Vectorization
Gradient Descent
Types
  1. Batch G.D
  2. Stochastic G.D (S.G.D)
  3. Mini-Batch G.D
Resampling
Types;
  1. cross-validation
    • a. Holdout
    • b. leave one-off
    • c. k. fold
  2. Bootstrap Resampling.
Resampling methods
1 cross validation Dataset
↙         ↘
-> 70%          -> 30% validation
a. Hold out -> random (level by level)
b. Leave one-off out (cross-validation) (LOOCV)
c. K-fold.
2. Bootstrap Resampling
without repeating data
Linear Discriminant Analysis (LDA)
parametric Model Non-Parametric Model
1. fixed no. of Parameter Flexible
2. ASSUME a specific Relationship does not assume a fixed relationship
3. Simple and faster More flexible and slower.
4. Need less training data Needs more training data
Unit-2
Principal Component Analysis (PCA)
How PCA works (steps)
  1. collect the dataset
  2. Find relationship (correlation) between features.
  3. create New feature (principle components)
  4. keep the components with Most importance.
  5. Remove less importance components.
Definition;
  • PCA is A dimensionality reduction techquie.
  • used to reduce No. of input features while keeping as Much important information as possible
  • PCA converts Many Related features into small of features without losing Much information.
  • New features is called principal component.