Answer:
Explanation:
1. Mechanical waves require material medium for their propagation while electromagnetic waves do not require material medium for their propagation.
2. Mechanical waves can either be transverse or longitudinal while electromagnetic waves are transverse.(Transverse waves are waves in which the vibration of the particules of the medium is perpendicular to the direction of the motion of wave. E.g water waves, waves of a plucked string and all electromagnetic waves RIVUXG . Longitudinal waves are waves whose vibration are parallel to the direction of the motion of the medium e.g waves in strings, sound waves.e.t.c)
There are many Autotrophs than consumers because : Autotrophs are the bases of any food chain/web
<h3>Function of Autotrophs </h3>
Autotrophs capture the energy required for every food chain or web from which sustains other organisms in the food chain form sunlight and chemicals. Also there is always more biomass present in the lower levels of the trophic system as biomass decreases as we move up the food chain.
Since autotrophs are producers, for a healthy food chain there would be more autotrophs than consumers.
Hence we can conclude that There are many Autotrophs than consumers because : Autotrophs are the bases of any food chain/web
Learn more about Autotrophs : brainly.com/question/10253663
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<u>Series Circuit.</u>
There is only one path in which electrons can flow in a circuit.
Algebraic Formulas (for n number of components)
I = I1 = I2 = I3 =In
V= V1 + V2 + V3 + - - - + Vn
Req = R1 + R2 + R3 + - - - + Rn
Vn = I Rn
<u>Parallel Circuit.</u>
There is more than one path in which electrons can flow in a circuit.
Algebraic Formulas
I = I1 + I2 + I3 = - - - - - +In
V= V1 = V2 = V3 + - - - - - = Vn
1/Req = 1/R1 + 1/R2 + 1/R3 + - - - - - + 1/Rn
V = In Rn
Answer:
c. Performs better on training data as the training process proceeds, while performing worse on a held-out test data
Explanation:
An over-fitted model is one that will perform best on training but would fail or do worse on a held-out test data.
Such models are optimum for a just a particular set of data but would grossly failed when extrapolated to some other data set not novel to it.
- Over-fitting a model implies that a model closely corresponds to a set of data but would not perform well with others.
- It is usually as a result of a model adapting the noise and other details of a particular data set and thereby incorporates it.
- This makes it difficult for the model to fit into another data set.
If light travels 290,000,000 meters in 1 second, simply divide 1 second by 290,000,000 to find the time it takes to travel 1 meter.
1 / 290,000,000 = 0.000000003448 seconds (<em>or 3.5 nano seconds</em>)
