Given a text T, and several options, according to the question posed in the text T to choose the most appropriate option from the options as the answer, please directly output the most appropriate option, do not need to output the explanation.

Alef Associates manages a long-only fund specializing in global smallcap equities. Since its founding a decade ago, Alef maintains a portfolio of 100 stocks (out of an eligible universe of about 10,000 stocks). Some of these holdings are the result of screening the universe for attractive stocks based on several ratios that use readily available market and accounting data; others are the result of investment ideas generated by Alef’s professional staff of five securities analysts and two portfolio managers.Although Alef’s investment performance has been good, its Chief Investment Officer, Paul Moresanu, is contemplating a change in the investment process aimed at achieving even better returns. After attending multiple workshops and being approached by data vendors, Moresanu feels that data science should play a role in the way Alef selects its investments. He has also noticed that much of Alef’s past outperformance is due to stocks that became takeover targets. After some research and reflection, Moresanu writes the following email to the Alef’s CEO.Subject: Investment Process ReorganizationI have been thinking about modernizing the way we select stock investments. Given that our past success has put Alef Associates in an excellent financial position, now seems to be a good time to invest in our future. What I propose is that we continue managing a portfolio of 100 global small-cap stocks but restructure our process to benefit from machine learning (ML). Importantly, the new process will still allow a role for human insight, for example, in providing domain knowledge. In addition, I think we should make a special effort to identify companies that are likely to be acquired. Specifically, I suggest following the four steps which would be repeated every quarter.Step 1 We apply ML techniques to a model including fundamental and technical variables (features) to predict next quarter’s return for each of the 100 stocks currently in our portfolio. Then, the 20 stocks with the lowest estimated return are identified for replacement.Step 2 We utilize ML techniques to divide our investable universe of about 10,000 stocks into 20 different groups, based on a wide variety of the most relevant financial and non-financial characteristics. The idea is to prevent unintended portfolio concentration by selecting stocks from each of these distinct groups.Step 3 For each of the 20 different groups, we use labeled data to train a model that will predict the five stocks (in any given group) that are most likely to become acquisition targets in the next one year.Step 4 Our five experienced securities analysts are each assigned four of the groups, and then each analyst selects their one best stock pick from each of their assigned groups. These 20 “high-conviction” stocks will be added to our portfolio (in replacement of the 20 relatively underperforming stocks to be sold in Step 1).A couple of additional comments related to the above:Comment 1 The ML algorithms will require large amounts of data. We would first need to explore using free or inexpensive historical datasets and then evaluate their usefulness for the ML-based stock selection processes before deciding on using data that requires subscription.Comment 2 As time passes, we expect to find additional ways to apply ML techniques to refine Alef’s investment processes.What do you think?Paul Moresanu
Assuming regularization is utilized in the machine learning technique used for executing Step 1, which of the following ML models would be least appropriate:
Option A:Regression tree with pruning.
Option B:LASSO with lambda (λ) equal to 0.
Option C:LASSO with lambda (λ) between 0.5 and 1.

Answer: B