Week 1: Missing Values: Remove Features if majority of in- Example: Consider dataset with sunburned people: 3 out of 8 get sun- is assigned to the instance. Overall, bagging decreases the variance.
stances are missing; Removing Instances is not ideal with limited burned (Entropy = 0.9544). For hair color, blond has 4 people with a Boosting: Prioritize misclassified instances by weight adjustment.
data; Replacing with mean, median, or mode introduces noise; Au- split of 2 sunburned, 2 not (Entropy = 1); brown has 3 people, none Retrain model till criterion is met. Boosting is a sequential ensemble
toencoders are unsupervised neural nets with Encoder & Decoder sunburned (Entropy = 0); red-haired is 1 person, not sunburned (En- method that iteratively adjusts the weight of observation as per the
components. tropy = 0). Compute the Information Gain for the attribute ”Hair” last classification. If an instance is incorrectly classified, it increases
x−min(x) using: its weight. The term ‘boosting’ refers to methods that convert a weak
Normalization: Transform values to x′ = max(x)−min(x) .
4 3 1 learner to a stronger one. It usually decreases the bias error and builds
Standardization: Shift values by mean and scale by standard devi- IG(Hair) = 0.9544 − ×1+ ×0+ ×0 strong predictive models.
ation: z = x−µ . 8 8 8
σ Distance Functions:
Pearson’s Correlation: Solution: IG(Hair) = 0.4544
P Bayesian Learning: Classify based on feature frequencies, assuming
(xi − x̄)(yi − ȳ) independence. v
R = pP u d d
(xi − x̄)2 (yi − ȳ)2 Naive Bayes: Naı̈ve Bayes does not impose any restrictions on the
P uX X
number of decision classes to be produced, although the example in Euclidean(x, y) = t (xi − yi )2 , Manhattan(x, y) = |xi − yi |
the lecture referred to a binary classification problem. The remaining i=1 i=1
Chi-squared:
X (Oij − Eij )2 options hold (this supervised learning method assumes that features d d
! p1
χ2 = have the same importance and are independent).
Hamm.(x, y) =
X
I(xi ̸= yi ), Mink.(x, y, p) =
X
|xi − yi | p
Eij
i=1 i=1
rowi ×colj P (H) × P (E|H)
where Eij = .
total
P (H|E) =
Example: Consider the contingency table: P (E)
Week 3 Evaluation and model selection: Generalization Ca-
Dogs Cats Rabbits Example: P r[E1 |H] might be P r[animal = cat|pet = yes]. pability: Model’s performance on unseen data. Hyperparameters
Shelter A 5 3 2 Given a dataset with the following entries for playing outside based regulate model construction.
Shelter B 6 8 4 on weather outlook and temperature:
Outlook Temperature Play Performance Measures assess model’s predictive capabilities.
To calculate χ2 : 1. Compute row, column and total sums. 2. Cal- Sunny Hot Yes Data Splitting: Hold-out splits dataset into training, validation,
rowi ×colj Overcast Mild Yes and test. Risks: Improper representation, unrealistically high results.
culate expected frequencies: Eij = . 3. Plug into the chi-
total Rainy Cool No
squared formula. Sunny Mild Yes Stratification matches sample’s class distribution to the entire pop-
Using the formula, for dogs at Shelter A: Sunny Cool Yes ulation.
Rainy Mild No K-fold Cross-validation averages results over k iterations using k-1
(5 + 6) × (5 + 3) 11 × 8 From the data, we derive the probabilities: P (Play=Yes) = for training.
E11 = = = 3.93
28 28 4
P (Play=No) = 2
P (Outlook=Sunny—Play=Yes) = 3
Nested K-fold CV: Inner CV used to tune hyperparameters (vali-
6 6 4
1 dation) to get best model for outer CV.
Continue this process for all cells. P (Temperature=Cool—Play=Yes) = 4
The chi-squared statistic is: Using the Naı̈ve Bayes formula for the likelihood someone Hyperparameter Tuning: Grid Search: All combinations. Ran-
will play outside when the outlook is sunny and temperature dom Search: Subset of combinations.
X (Oij − Eij )2 is cool: P (Play=Yes—Outlook=Sunny, Temperature=Cool) =
χ2 = P (Outlook=Sunny—Play=Yes)×P (Temperature=Cool—Play=Yes)× Confusion Matrix:
i,j
Eij P (Play=Yes)
Substituting in the values:
Predict Cat Predict Dog
Compute χ2 for all animals and sum them to get the final statistic. P (Play=Yes|Outlook=Sunny, Temperature=Cool) = 43 × 14 × 46 = 81 True Cat TP FN
Encoding: Label Encoding for ordinal data, e.g., education level; Then do the same for P (Play=NO—Outlook=Sunny, Temperature=Cool) True Dog FP TN
One-hot Encoding for nominal data, e.g., cat/dog/rabbit. Under- and normalize to get a result between 0 and 1.
sampling for large datasets can cause data loss and lower accuracy; Lazy Learning: Similarity function crucial. KNN uses K-nearest
Oversampling can lead to overfitting with small datasets. neighbors and a distance function. Sensitive to outliers and distance
function selection.
Week 2 Pattern classification: Rule-based Learning: Classify Random Forest: Aggregates decision trees’ outputs through major- TP + TN TP
with rules from features and values. Decision trees are popular. Data ity vote. More trees may overfit. uses bagging Shallow decision trees Accuracy = , Recall =
splits based on high entropy attributes. T otal TP + FN
might lead to overly simple models unable to fit the data. A model
Entropy: that underfits will have high training and high test errors. Hence, poor TP (1 + β 2 ) × precision × recall
X P recision = , Fβ =
Entropy(P ) = − pi log pi performance on training and test sets indicates underfitting, which TP + FP β 2 × precision + recall
i means the hypotheses are not complex enough to include the true
but unknown prediction function. The shallower the tree, the less
Weighted Entropy: variance we have in our predictions. However, we can start to inject
too much bias at some point as shallow trees (e.g., stumps) cannot Bias-Variance Trade-off: Bias (fit) low bias fits data well. High
capture interactions and complex patterns present in our data. Bag- bias → model is too simple for the data (underfitting).) vs. Variance
X xi
inf o(x) = × entropyi
nobs ging generates additional data for training from the dataset (using (consistency training to test) High variance → models the random
random sampling with replacement). Every element is equally prob- noise in the training data (overfitting).
Information Gain: able to be selected. Such datasets are used to train multiple models Overfitting Solutions: Pruning in Trees: Simplify by removing less
in parallel. The average of all the predictions from different ensemble impactful subtrees. Higher k in kNN: Larger k reduces overfitting
gain(fi ) = inf o(root) − inf o(fi ) models is calculated. The decision class resulting from a majority vote risk.
