shazadabu2

Model	Boundary	Needs Scaling	Probabilities	Strengths	Weak Spots
Logistic Reg	Linear	✅	✅ (softmax)	Fast, interpretable, calibrated	Nonlinear patterns
SVM (Linear)	Linear max-margin	✅	⚠️ (enable)	Robust separator, few params	Overlap regions
SVM (RBF)	Nonlinear	✅	⚠️	Powerful on small data	Tune C,gamma
KNN	Nonlinear (data-driven)	✅	❌	Simple, local structure	Slow predict, noisy, scales matter
Decision Tree	Nonlinear	❌	✅ (leaf freq)	Explainable

shazadabu2

ID	Outlook	Temperature	Play Tennis
1	Sunny	Hot	No
2	Sunny	Hot	No
3	Overcast	Hot	Yes
4	Rain	Mild	Yes
5	Rain	Cool	Yes
6	Rain	Hot	No
7	Overcast	Cool	Yes
8	Sunny	Mild	No

shazadabu2

Kernel	Best For	Parameters to Tune	Risk
Linear	Linearly separable data	None	Poor for complex data
Polynomial	Structured patterns	c, d	Overfitting with high d
RBF	Complex, non-linear data	γ	Sensitive to γ
Sigmoid	Neural network-like problems	α	Can be unstable

shazadabu2

Model Family	Scale?	Notes
KNN / K-Means	✅	Distances must be comparable across features
SVM (linear/RBF)	✅	Margin/RBF kernel sensitive to scale
Logistic/Linear (with penalties)	✅	Coefficients & penalties become comparable
Neural Nets	✅	Helps optimization & stability
Trees / Random Forest / Gradient boost decision tree	❌	Split thresholds are rank-based
Naive Bayes	❌	Usually fine unscaled (Gaussian NB ok either way)

shazadabu2

Scaler Type	Outlier Robust?	Distribution Goal	Range/Bias	Sparse-safe
StandardScaler	❌	~Gaussian (z-scores)	Mean 0, Var 1	⚠️ (with_mean=False for sparse)
MinMaxScaler	❌	Preserve shape	[0,1] (or custom)	✅
RobustScaler	✅	Median/IQR-based	No fixed range	⚠️
MaxAbsScaler	❌	Preserve signs	[-1,1]	✅
PowerTransformer	⚠️	More Gaussian (de-skew)	No fixed range	❌
QuantileTransformer	✅	Uniform/Normal via ranks	[0,1] or Normal	❌
Normalizer (row-wise)

shazadabu2

	count	mean	std	min	25%	50%	75%	max	skew	kurtosis
symboling	201.0	0.840796	1.254802	-2.000000	0.000000	1.000000	2.000000	3.000000	0.197370	-0.707178
normalized-losses	201.0	122.000000	31.996250	65.000000	101.000000	122.000000	137.000000	256.000000	0.846546	1.319068
wheel-base	201.0	98.797015	6.066366	86.600000	94.500000	97.000000	102.400000	120.900000	1.031261	0.948445
length	201.0	0.837102	0.059213	0.678039	0.801538	0.832292	0.881788	1.000000	0.154446	-0.065192
width	201.0	0.915126	0.029187	0.837500	0.890278	0.909722	0.925000	1.000000	0.875029	0.678655

shazadabu2

symboling	normalized-losses	make	aspiration	num-of-doors	body-style	drive-wheels	engine-location	wheel-base	length	...	compression-ratio	horsepower	peak-rpm	city-mpg	highway-mpg	price	city-L/100km	horsepower-binned	diesel	gas
3	122	alfa-romero	std	two	convertible	rwd	front	88.6	0.81115	...	9.0	111.0	5000.0	21	27	13495.0	11.190476	Medium	0	1
3	122	alfa-romero	std	two	convertible	rwd	front	88.6	0.81115	...	9.0	111.0